Heat map generating system and methods for use therewith

ABSTRACT

A multi-label heat map generating system is operable to receive a plurality of medical scans and a corresponding plurality of medical labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the medical labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Preliminary heat map visualization data can be generated for transmission to a client device based on the probability matrix data. Heat map visualization data can be generated via a post-processing of the preliminary heat map visualization data to mitigate heat map artifacts.

CROSS REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation-in-part of U.S. Utility applicationSer. No. 16/299,779, entitled “MULTI-LABEL HEAT MAP DISPLAY SYSTEM”,filed Mar. 12, 2019, which claims priority pursuant to 35 U.S.C. §119(e) to U.S. Provisional Application No. 62/770,334, entitled “LESIONTRACKING SYSTEM”, filed Nov. 21, 2018, both of which are herebyincorporated herein by reference in their entirety and made part of thepresent U.S. Utility patent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND Technical Field

This invention relates generally to medical imaging devices andknowledge-based systems used in conjunction with client/server networkarchitectures.

Description of Related Art

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a medical scanprocessing system;

FIG. 2A is a schematic block diagram of a client device in accordancewith various embodiments;

FIG. 2B is a schematic block diagram of one or more subsystems inaccordance with various embodiments;

FIG. 3 is a schematic block diagram of a database storage system inaccordance with various embodiments;

FIG. 4A is schematic block diagram of a medical scan entry in accordancewith various embodiments;

FIG. 4B is a schematic block diagram of abnormality data in accordancewith various embodiments;

FIG. 5A is a schematic block diagram of a user profile entry inaccordance with various embodiments;

FIG. 5B is a schematic block diagram of a medical scan analysis functionentry in accordance with various embodiments;

FIGS. 6A-6B are schematic block diagram of a medical scan diagnosingsystem in accordance with various embodiments;

FIG. 7A is a flowchart representation of an inference step in accordancewith various embodiments;

FIG. 7B is a flowchart representation of a detection step in accordancewith various embodiments;

FIGS. 8A-8F are schematic block diagrams of a medical picture archiveintegration system in accordance with various embodiments;

FIG. 9 is a flowchart representation of a method for execution by amedical picture archive integration system in accordance with variousembodiments;

FIG. 10A is a schematic block diagram of a de-identification system inaccordance with various embodiments;

FIG. 10B is an illustration of an example of anonymizing patientidentifiers in image data of a medical scan in accordance with variousembodiments;

FIG. 11 presents a flowchart illustrating a method for execution by ade-identification system in accordance with various embodiments;

FIGS. 12A-12C are a schematic block diagrams of a multi-label medicalscan analysis system in accordance with various embodiments;

FIG. 13A illustrates an example embodiment of a model that is beutilized by the multi-label medical scan analysis system 3002;

FIGS. 13B-13G illustrate example embodiments of the multi-label medicalscan analysis system 3002;

FIG. 14A is a schematic block diagram of a multi-label heat map displaysystem in accordance with various embodiments;

FIGS. 14B-14C illustrate example interfaces generated for display by amulti-label heat map display system in accordance with variousembodiments;

FIG. 15A is a schematic block diagram of a retroactive discrepancyflagging system in accordance with various embodiments;

FIG. 15B is a schematic block diagram of a factor detection system inaccordance with various embodiments; and

FIG. 16 is a flowchart illustrating a method for execution by amulti-label heat map generating system in accordance with variousembodiments.

DETAILED DESCRIPTION

The present U.S. Utility patent application is related to U.S. Utilityapplication Ser. No. 16/919,362, entitled “SYSTEM WITH RETROACTIVEDISCREPANCY FLAGGING AND METHODS FOR USE THEREWITH”, filed 2 Jul. 2020and U.S. Utility application Ser. No. 15/627,644, entitled “MEDICAL SCANASSISTED REVIEW SYSTEM”, filed 20 Jun. 2017, which claims prioritypursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No.62/511,150, entitled “MEDICAL SCAN ASSISTED REVIEW SYSTEM AND METHODS”,filed 25 May 2017, both of which are hereby incorporated herein byreference in their entirety and made part of the present U.S. Utilitypatent application for all purposes.

FIG. 1 presents a medical scan processing system 100, which can includeone or more medical scan subsystems 101 that communicate bidirectionallywith one or more client devices 120 via a wired and/or wireless network150. The medical scan subsystems 101 can include a medical scan assistedreview system 102, medical scan report labeling system 104, a medicalscan annotator system 106, a medical scan diagnosing system 108, amedical scan interface feature evaluator system 110, a medical scanimage analysis system 112, a medical scan natural language analysissystem 114, and/or a medical scan comparison system 116. Some or all ofthe subsystems 101 can utilize the same processing devices, memorydevices, and/or network interfaces, for example, running on a same setof shared servers connected to network 150. Alternatively or inaddition, some or all of the subsystems 101 be assigned their ownprocessing devices, memory devices, and/or network interfaces, forexample, running separately on different sets of servers connected tonetwork 150. Some or all of the subsystems 101 can interact directlywith each other, for example, where one subsystem's output istransmitted directly as input to another subsystem via network 150.Network 150 can include one or more wireless and/or wired communicationsystems; one or more non-public intranet systems and/or public internetsystems; and/or one or more local area networks (LAN) and/or wide areanetworks (WAN).

The medical scan processing system 100 can further include a databasestorage system 140, which can include one or more servers, one or morememory devices of one or more subsystems 101, and/or one or more othermemory devices connected to network 150. The database storage system 140can store one or more shared databases and/or one or more files storedon one or more memory devices that include database entries as describedherein. The shared databases and/or files can each be utilized by someor all of the subsystems of the medical scan processing system, allowingsome or all of the subsystems and/or client devices to retrieve, edit,add, or delete entries to the one or more databases and/or files.

The one or more client devices 120 can each be associated with one ormore users of one or more subsystems of the medical scan processingsystem. Some or all of the client devices can be associated withhospitals or other medical institutions and/or associated with medicalprofessionals, employees, or other individual users for example, locatedat one or more of the medical institutions. Some of the client devices120 can correspond to one or more administrators of one or moresubsystems of the medical scan processing system, allowingadministrators to manage, supervise, or override functions of one ormore subsystems for which they are responsible.

Some or all of the subsystems 101 of the medical scan processing system100 can include a server that presents a website for operation via abrowser of client devices 120. Alternatively or in addition, each clientdevice can store application data corresponding to some or allsubsystems, for example, a subset of the subsystems that are relevant tothe user in a memory of the client device, and a processor of the clientdevice can display the interactive interface based on instructions inthe interface data stored in memory. For example, the website presentedby a subsystem can operate via the application. Some or all of the websites presented can correspond to multiple subsystems, for example,where the multiple subsystems share the server presenting the web site.Furthermore, the network 150 can be configured for secure and/orauthenticated communications between the medical scan subsystems 101,the client devices 120 and the database storage system 140 to protectthe data stored in the database storage system and the data communicatedbetween the medical scan subsystems 101, the client devices 120 and thedatabase storage system 140 from unauthorized access.

The medical scan assisted review system 102 can be used to aid medicalprofessionals or other users in diagnosing, triaging, classifying,ranking, and/or otherwise reviewing medical scans by presenting amedical scan for review by a user by transmitting medical scan data of aselected medical scan and/or interface feature data of selectedinterface features of to a client device 120 corresponding to a user ofthe medical scan assisted review system for display via a display deviceof the client device. The medical scan assisted review system 102 cangenerate scan review data for a medical scan based on user input to theinteractive interface displayed by the display device in response toprompts to provide the scan review data, for example, where the promptscorrespond to one or more interface features.

The medical scan assisted review system 102 can be operable to receive,via a network, a medical scan for review. Abnormality annotation datacan be generated by identifying one or more of abnormalities in themedical scan by utilizing a computer vision model that is trained on aplurality of training medical scans. The abnormality annotation data caninclude location data and classification data for each of the pluralityof abnormalities and/or data that facilitates the visualization of theabnormalities in the scan image data. Report data including textdescribing each of the plurality of abnormalities is generated based onthe abnormality data. The visualization and the report data, which cancollectively be displayed annotation data, can be transmitted to aclient device. A display device associated with the client device candisplay the visualization in conjunction with the medical scan via aninteractive interface, and the display device can further display thereport data via the interactive interface.

In various embodiments, longitudinal data, such as one or moreadditional scans of longitudinal data 433 of the medical scan or ofsimilar scans, can be displayed in conjunction with the medical scanautomatically, or in response to the user electing to view longitudinaldata via user input. For example, the medical scan assisted reviewsystem can retrieve a previous scan or a future scan for the patientfrom a patient database or from the medical scan database automaticallyor in response to the user electing to view past patient data. One ormore previous scans can be displayed in one or more correspondingwindows adjacent to the current medical scan. For example, the user canselect a past scan from the longitudinal data for display. Alternativelyor in addition, the user can elect longitudinal parameters such asamount of time elapsed, scan type, electing to select the most recentand/or least recent scan, electing to select a future scan, electing toselect a scan at a date closest to the scan, or other criteria, and themedical scan assisted review system can automatically select a previousscan that compares most favorably to the longitudinal parameters. Theselected additional scan can be displayed in an adjacent windowalongside the current medical scan. In some embodiments, multipleadditional scans will be selected and can be displayed in multipleadjacent windows.

In various embodiments, a first window displaying an image slice 412 ofthe medical scan and an adjacent second window displaying an image sliceof a selected additional scan will display image slices 412 determinedto corresponding with the currently displayed slice 412 of the medicalscan. As described with respect to selecting a slice of a selectedsimilar medical scan for display, this can be achieved based onselecting the image slice with a matching slice number, based onautomatically determining the image slice that most closely matches theanatomical region corresponding to the currently displayed slice of thecurrent scan, and/or based on determining the slice in the previous scanwith the most similar view of the abnormality as the currently displayedslice. The user can use a single scroll bar or other single user inputindication to jump to a different image slice, and the multiple windowscan simultaneously display the same numbered image slice, or can scrollor jump by the same number of slices if different slice numbers areinitially displayed. In some embodiments, three or more adjacent windowscorresponding to the medical scan and two or more additional scans aredisplayed, and can all be controlled with the single scroll bar in asimilar fashion.

The medical scan assisted review system 102 can automatically detectprevious states of the identified abnormalities based on the abnormalitydata, such as the abnormality location data. The detected previousstates of the identified abnormality can be circled, highlighted, orotherwise indicated in their corresponding window. The medical scanassisted review system 102 can retrieve classification data for theprevious state of the abnormality by retrieving abnormality annotationdata 442 of the similar abnormality mapped to the previous scan from themedical scan database 342. This data may not be assigned to the previousscan, and the medical scan assisted review system can automaticallydetermine classification or other diagnosis data for the previousmedical scan by utilizing the medical scan image analysis system asdiscussed. Alternatively or in addition, some or all of the abnormalityclassification data 445 or other diagnosis data 440 for the previousscan can be assigned values determined based on the abnormalityclassification data or other diagnosis data determined for the currentscan. Such abnormality classification data 445 or other diagnosis data440 determined for the previous scan can be mapped to the previous scan,and or mapped to the longitudinal data 433, in the database and/ortransmitted to a responsible entity via the network.

The medical assisted review system can automatically generate statechange data such as a change in size, volume, malignancy, or otherchanges to various classifiers of the abnormality. This can be achievedby automatically comparing image data of one or more previous scans andthe current scan and/or by comparing abnormality data of the previousscan to abnormality data of the current scan. In some embodiments, suchmetrics can be calculated by utilizing the medical scan similarityanalysis function, for example, where the output of the medical scansimilarity analysis function such as the similarity score indicatesdistance, error, or other measured discrepancy in one or moreabnormality classifier categories 444 and/or abnormality patterncategories 446. This calculated distance, error, or other measureddiscrepancy in each category can be used to quantify state change data,indicate a new classifier in one or more categories, to determine if acertain category has become more or less severe, or otherwise determinehow the abnormality has changed over time. In various embodiments, thisdata can be displayed in one window, for example, where an increase inabnormality size is indicated by overlaying or highlighting an outlineof the current abnormality over the corresponding image slice of theprevious abnormality, or vice versa. In various embodiments whereseveral past scans are available, such state change data can bedetermined over time, and statistical data showing growth rate changesover time or malignancy changes over time can be generated, for example,indicating if a growth rate is lessening or worsening over time. Imageslices corresponding to multiple past scans can be displayed insequence, for example, where a first scroll bar allows a user to scrollbetween image slice numbers, and a second scroll bar allows a user toscroll between the same image slice over time. In various embodimentsthe abnormality data, heat map data, or other interface features will bedisplayed in conjunction with the image slices of the past image data.

The medical scan report labeling system 104 can be used to automaticallyassign medical codes to medical scans based on user identified keywords,phrases, or other relevant medical condition terms of natural text datain a medical scan report of the medical scan, identified by users of themedical scan report labeling system 104. The medical scan reportlabeling system 104 can be operable to transmit a medical report thatincludes natural language text to a first client device for display.Identified medical condition term data can be received from the firstclient device in response. An alias mapping pair in a medical labelalias database can be identified by determining that a medical conditionterm of the alias mapping pair compares favorably to the identifiedmedical condition term data. A medical code that corresponds to thealias mapping pair and a medical scan that corresponds to the medicalreport can be transmitted to a second client device of an expert userfor display, and accuracy data can be received from the second clientdevice in response. The medical code is mapped to the first medical scanin a medical scan database when the accuracy data indicates that themedical code compares favorably to the medical scan.

The medical scan annotator system 106 can be used to gather annotationsof medical scans based on review of the medical scan image data by usersof the system such as radiologists or other medical professionals.Medical scans that require annotation, for example, that have beentriaged from a hospital or other triaging entity, can be sent tomultiple users selected by the medical scan annotator system 106, andthe annotations received from the multiple medical professionals can beprocessed automatically by a processing system of the medical scanannotator system, allowing the medical scan annotator system toautomatically determine a consensus annotation of each medical scan.Furthermore, the users can be automatically scored by the medical scanannotator system based on how closely their annotation matches to theconsensus annotation or some other truth annotation, for example,corresponding to annotations of the medical scan assigned a truth flag.Users can be assigned automatically to annotate subsequent incomingmedical scans based on their overall scores and/or based on categorizedscores that correspond to an identified category of the incoming medicalscan.

The medical scan annotator system 106 can be operable to select amedical scan for transmission via a network to a first client device anda second client device for display via an interactive interface, andannotation data can be received from the first client device and thesecond client device in response. Annotation similarity data can begenerated by comparing the first annotation data to the secondannotation data, and consensus annotation data can be generated based onthe first annotation data and the second annotation data in response tothe annotation similarity data indicating that the difference betweenthe first annotation data and the second annotation data comparesfavorably to an annotation discrepancy threshold. The consensusannotation data can be mapped to the medical scan in a medical scandatabase.

A medical scan diagnosing system 108 can be used by hospitals, medicalprofessionals, or other medical entities to automatically produceinference data for given medical scans by utilizing computer visiontechniques and/or natural language processing techniques. Thisautomatically generated inference data can be used to generate and/orupdate diagnosis data or other corresponding data of correspondingmedical scan entries in a medical scan database. The medical scandiagnosing system can utilize a medical scan database, user database,and/or a medical scan analysis function database by communicating withthe database storage system 140 via the network 150, and/or can utilizeanother medical scan database, user database, and/or function databasestored in local memory.

The medical scan diagnosing system 108 can be operable to receive amedical scan. Diagnosis data of the medical scan can be generated byperforming a medical scan inference function on the medical scan. Thefirst medical scan can be transmitted to a first client deviceassociated with a user of the medical scan diagnosing system in responseto the diagnosis data indicating that the medical scan corresponds to anon-normal diagnosis. The medical scan can be displayed to the user viaan interactive interface displayed by a display device corresponding tothe first client device. Review data can be received from the firstclient device, where the review data is generated by the first clientdevice in response to a prompt via the interactive interface. Updateddiagnosis data can be generated based on the review data. The updateddiagnosis data can be transmitted to a second client device associatedwith a requesting entity.

A medical scan interface feature evaluating system 110 can be usedevaluate proposed interface features or currently used interfacefeatures of an interactive interface to present medical scans for reviewby medical professionals or other users of one or more subsystems 101.The medical scan interface feature evaluator system 110 can be operableto generate an ordered image-to-prompt mapping by selecting a set ofuser interface features to be displayed with each of an ordered set ofmedical scans. The set of medical scans and the ordered image-to-promptmapping can be transmitted to a set of client devices. A set ofresponses can be generated by each client device in response tosequentially displaying each of the set of medical scans in conjunctionwith a mapped user interface feature indicated in the orderedimage-to-prompt mapping via a user interface. Response score data can begenerated by comparing each response to truth annotation data of thecorresponding medical scan. Interface feature score data correspondingto each user interface feature can be generated based on aggregating theresponse score data, and is used to generate a ranking of the set ofuser interface features.

A medical scan image analysis system 112 can be used to generate and/orperform one or more medical scan image analysis functions by utilizing acomputer vision-based learning algorithm 1350 on a training set ofmedical scans with known annotation data, diagnosis data, labelingand/or medical code data, report data, patient history data, patientrisk factor data, and/or other metadata associated with medical scans.These medical scan image analysis functions can be used to generateinference data for new medical scans that are triaged or otherwiserequire inferred annotation data, diagnosis data, labeling and/ormedical code data, and/or report data. For example, some medical scanimage analysis functions can correspond to medical scan inferencefunctions of the medical scan diagnosing system or other medical scananalysis functions of a medical scan analysis function database. Themedical scan image analysis functions can be used to determine whetheror not a medical scan is normal, to detect the location of anabnormality in one or more slices of a medical scan, and/or tocharacterize a detected abnormality. The medical scan image analysissystem can be used to generate and/or perform computer vision basedmedical scan image analysis functions utilized by other subsystems ofthe medical scan processing system as described herein, aiding medicalprofessionals to diagnose patients and/or to generate further data andmodels to characterize medical scans. The medical scan image analysissystem can include a processing system that includes a processor and amemory that stores executable instructions that, when executed by theprocessing system, facilitate performance of operations.

The medical scan image analysis system 112 can be operable to receive aplurality of medical scans that represent a three-dimensional anatomicalregion and include a plurality of cross-sectional image slices. Aplurality of three-dimensional subregions corresponding to each of theplurality of medical scans can be generated by selecting a proper subsetof the plurality of cross-sectional image slices from each medical scan,and by further selecting a two-dimensional subregion from each propersubset of cross-sectional image slices. A learning algorithm can beperformed on the plurality of three-dimensional subregions to generate aneural network. Inference data corresponding to a new medical scanreceived via the network can be generated by performing an inferencealgorithm on the new medical scan by utilizing the neural network. Aninferred abnormality can be identified in the new medical scan based onthe inference data.

The medical scan natural language analysis system 114 can determine atraining set of medical scans with medical codes determined to be truthdata. Corresponding medical reports and/or other natural language textdata associated with a medical scan can be utilized to train a medicalscan natural language analysis function by generating a medical reportnatural language model. The medical scan natural language analysisfunction can be utilized to generate inference data for incoming medicalreports for other medical scans to automatically determine correspondingmedical codes, which can be mapped to corresponding medical scans.Medical codes assigned to medical scans by utilizing the medical reportnatural language model can be utilized by other subsystems, for example,to train other medical scan analysis functions, to be used as truth datato verify annotations provided via other subsystems, to aid indiagnosis, or otherwise be used by other subsystems as described herein.

A medical scan comparison system 116 can be utilized by one or moresubsystems to identify and/or display similar medical scans, forexample, to perform or determine function parameters for a medical scansimilarity analysis function, to generate or retrieve similar scan data,or otherwise compare medical scan data. The medical scan comparisonsystem 116 can also utilize some or all features of other subsystems asdescribed herein. The medical scan comparison system 116 can be operableto receive a medical scan via a network and can generate similar scandata. The similar scan data can include a subset of medical scans from amedical scan database and can be generated by performing an abnormalitysimilarity function, such as medical scan similarity analysis function,to determine that a set of abnormalities included in the subset ofmedical scans compare favorably to an abnormality identified in themedical scan. At least one cross-sectional image can be selected fromeach medical scan of the subset of medical scans for display on adisplay device associated with a user of the medical scan comparisonsystem in conjunction with the medical scan.

FIG. 2A presents an embodiment of client device 120. Each client device120 can include one or more client processing devices 230, one or moreclient memory devices 240, one or more client input devices 250, one ormore client network interfaces 260 operable to more support one or morecommunication links via the network 150 indirectly and/or directly,and/or one or more client display devices 270, connected via bus 280.Client applications 202, 204, 206, 208, 210, 212, 214, and/or 216correspond to subsystems 102, 104, 106, 108, 110, 112, 114, and/or 116of the medical scan processing system respectfully. Each client device120 can receive the application data from the corresponding subsystemvia network 150 by utilizing network interface 260, for storage in theone or more memory devices 240. In various embodiments, some or allclient devices 120 can include a computing device associated with aradiologist, medical entity, or other user of one or more subsystems asdescribed herein.

The one or more processing devices 230 can display interactive interface275 on the one or more client display devices 270 in accordance with oneor more of the client applications 202, 204, 206, 208, 210, 212, 214,and/or 216, for example, where a different interactive interface 275 isdisplayed for some or all of the client applications in accordance withthe website presented by the corresponding subsystem 102, 104, 106, 108,110, 112, 114 and/or 116. The user can provide input in response to menudata or other prompts presented by the interactive interface via the oneor more client input devices 250, which can include a microphone, mouse,keyboard, touchscreen of display device 270 itself or other touchscreen,and/or other device allowing the user to interact with the interactiveinterface. The one or more processing devices 230 can process the inputdata and/or send raw or processed input data to the correspondingsubsystem, and/or can receive and/or generate new data in response forpresentation via the interactive interface 275 accordingly, by utilizingnetwork interface 260 to communicate bidirectionally with one or moresubsystems and/or databases of the medical scan processing system vianetwork 150.

FIG. 2B presents an embodiment of a subsystem 101, which can be utilizedin conjunction with subsystem 102, 104, 106, 108, 110, 112, 114 and/or116. Each subsystem 101 can include one or more subsystem processingdevices 235, one or more subsystem memory devices 245, and/or one ormore subsystem network interfaces 265, connected via bus 285. Thesubsystem memory devices 245 can store executable instructions that,when executed by the one or more subsystem processing devices 235,facilitate performance of operations by the subsystem 101, as describedfor each subsystem herein.

FIG. 3 presents an embodiment of the database storage system 140.Database storage system 140 can include at least one database processingdevice 330, at least one database memory device 340, and at least onedatabase network interface 360, operable to more support one or morecommunication links via the network 150 indirectly and/or directly, allconnected via bus 380. The database storage system 140 can store one ormore databases the at least one memory 340, which can include a medicalscan database 342 that includes a plurality medical scan entries 352, auser database 344 that includes a plurality of user profile entries 354,a medical scan analysis function database 346 that includes a pluralityof medical scan analysis function entries 356, an interface featuredatabase 348 can include a plurality of interface feature entries 358,and/or other databases that store data generated and/or utilized by thesubsystems 101. Some or all of the databases 342, 344, 346 and/or 348can consist of multiple databases, can be stored relationally ornon-relationally, and can include different types of entries anddifferent mappings than those described herein. A database entry caninclude an entry in a relational table or entry in a non-relationalstructure. Some or all of the data attributes of an entry 352, 354, 356,and/or 358 can refer to data included in the entry itself or that isotherwise mapped to an identifier included in the entry and can beretrieved from, added to, modified, or deleted from the database storagesystem 140 based on a given identifier of the entry. Some or all of thedatabases 342, 344, 346, and/or 348 can instead be stored locally by acorresponding subsystem, for example, if they are utilized by only onesubsystem.

The processing device 330 can facilitate read/write requests receivedfrom subsystems and/or client devices via the network 150 based onread/write permissions for each database stored in the at least onememory device 340. Different subsystems can be assigned differentread/write permissions for each database based on the functions of thesubsystem, and different client devices 120 can be assigned differentread/write permissions for each database. One or more client devices 120can correspond to one or more administrators of one or more of thedatabases stored by the database storage system, and databaseadministrator devices can manage one or more assigned databases,supervise assess and/or efficiency, edit permissions, or otherwiseoversee database processes based on input to the client device viainteractive interface 275.

FIG. 4A presents an embodiment of a medical scan entry 352, stored inmedical scan database 342, included in metadata of a medical scan,and/or otherwise associated with a medical scan. A medical scan caninclude imaging data corresponding to a CT scan, x-ray, MM, PET scan,Ultrasound, EEG, mammogram, or other type of radiological scan ormedical scan taken of an anatomical region of a human body, animal,organism, or object and further can include metadata corresponding tothe imaging data. Some or all of the medical scan entries can beformatted in accordance with a Digital Imaging and Communications inMedicine (DICOM) format or other standardized image format, and some ormore of the fields of the medical scan entry 352 can be included in aDICOM header or other standardized header of the medical scan. Medicalscans can be awaiting review or can have already been reviewed by one ormore users or automatic processes and can include tentative diagnosisdata automatically generated by a subsystem, generated based on userinput, and/or generated from another source. Some medical scans caninclude final, known diagnosis data generated by a subsystem and/orgenerated based on user input, and/or generated from another source, andcan included in training sets used to train processes used by one ormore subsystems such as the medical scan image analysis system 112and/or the medical scan natural language analysis system 114.

Some medical scans can include one or more abnormalities, which can beidentified by a user or can be identified automatically. Abnormalitiescan include nodules, for example malignant nodules identified in a chestCT scan. Abnormalities can also include and/or be characterized by oneor more abnormality pattern categories such as such as cardiomegaly,consolidation, effusion, emphysema, and/or fracture, for exampleidentified in a chest x-ray. Abnormalities can also include any otherunknown, malignant or benign feature of a medical scan identified as notnormal. Some scans can contain zero abnormalities, and can be identifiedas normal scans. Some scans identified as normal scans can includeidentified abnormalities that are classified as benign, and include zeroabnormalities classified as either unknown or malignant. Scansidentified as normal scans may include abnormalities that were notdetected by one or more subsystems and/or by an originating entity.Thus, some scans may be improperly identified as normal. Similarly,scans identified to include at least one abnormality may include atleast one abnormality that was improperly detected as an abnormality byone or more subsystems and/or by an originating entity. Thus, some scansmay be improperly identified as containing abnormalities.

As used herein, “normal” can be defined based on a colloquially definedradiologist definition, in an unbiased environment, that is inherentlylearned by the model based on this training data—rather than from anempirical definition. The definition of “normal” can be equated to, forexample, “an absence of clinically significant or actionable findings”.In used herein, “normal” scans may still include minor abnormalitiesthat would be deemed insignificant by the average radiologist—and wouldstill be considered normal—and consequently—considered to be absent ofabnormalities. Consider, for example, a chest CT scan of a low-riskpatient where the only abnormality is a solitary pulmonary nodule with adiameter of 3.2 mm. According to the guidelines of the FleischnerSociety, this nodule can be ignored (e.g. no routine follow-up required)because it is less than 6 mm. Under these definitions, the scan can belabelled “normal” and absent abnormalities.

Each medical scan entry 352 can be identified by its own medical scanidentifier 353, and can include or otherwise map to medical scan imagedata 410, and metadata such as scan classifier data 420, patient historydata 430, diagnosis data 440, annotation author data 450, confidencescore data 460, display parameter data 470, similar scan data 480,training set data 490, and/or other data relating to the medical scan.Some or all of the data included in a medical scan entry 352 can be usedto aid a user in generating or editing diagnosis data 440, for example,in conjunction with the medical scan assisted review system 102, themedical scan report labeling system 104, and/or the medical scanannotator system 106. Some or all of the data included in a medical scanentry 352 can be used to allow one or more subsystems 101, such asautomated portions of the medical scan report labeling system 104 and/orthe medical scan diagnosing system 108, to automatically generate and/oredit diagnosis data 440 or other data the medical scan. Some or all ofthe data included in a medical scan entry 352 can be used to train someor all medical scan analysis functions of the medical scan analysisfunction database 346 such as one or more medical scan image analysisfunctions, one or more medical scan natural language analysis functions,one or more medical scan similarity analysis functions, one or moremedical report generator functions, and/or one or more medical reportanalysis functions, for example, in conjunction with the medical scanimage analysis system 112, the medical scan natural language analysissystem 114, and/or the medical scan comparison system 116.

The medical scan entries 352 and the associated data as described hereincan also refer to data associated with a medical scan that is not storedby the medical scan database, for example, that is uploaded by a clientdevice for direct transmission to a subsystem, data generated by asubsystem and used as input to another subsystem or transmitted directlyto a client device, data stored by a Picture Archive and CommunicationSystem (PACS) communicating with the medical scan processing system 100,or other data associated with a medical scan that is received and orgenerated without being stored in the medical scan database 342. Forexample, some or all of the structure and data attributes described withrespect to a medical scan entry 352 can also correspond to structureand/or data attribute of data objects or other data generated by and/ortransmitted between subsystems and/or client devices that correspond toa medical scan. Herein, any of the data attributes described withrespect to a medical scan entry 352 can also correspond to dataextracted from a data object generated by a subsystem or client deviceor data otherwise received from a subsystem, client device, or othersource via network 150 that corresponds to a medical scan.

The medical scan image data 410 can include one or more imagescorresponding to a medical scan. The medical scan image data 410 caninclude one or more image slices 412, for example, corresponding to asingle x-ray image, a plurality of cross-sectional, tomographic imagesof a scan such as a CT scan, or any plurality of images taken from thesame or different point at the same or different angles. The medicalscan image data 410 can also indicate an ordering of the one or moreimage slices 412. Herein, a “medical scan” can refer a full scan of anytype represented by medical scan image data 410. Herein, an “imageslice” can refer to one of a plurality of cross-sectional images of themedical scan image data 410, one of a plurality of images taken fromdifferent angles of the medical scan image data 410, and/or the singleimage of the medical scan image data 410 that includes only one image.Furthermore “plurality of image slices” can refer to all of the imagesof the associated medical scan, and refers to only a single image if themedical scan image data 410 includes only one image. Each image slice412 can include a plurality of pixel values 414 mapped to each pixel ofthe image slice. Each pixel value can correspond to a density value,such as a Hounsfield value or other measure of density. Pixel values canalso correspond to a grayscale value, a RGB (Red-Green-Blue) or othercolor value, or other data stored by each pixel of an image slice 412.

Scan classifier data 420 can indicate classifying data of the medicalscan. Scan classifier data can include scan type data 421, for example,indicating the modality of the scan. The scan classifier data canindicate that the scan is a CT scan, x-ray, MM, PET scan, Ultrasound,EEG, mammogram, or other type of scan. Scan classifier data 420 can alsoinclude anatomical region data 422, indicating for example, the scan isa scan of the chest, head, right knee, or other anatomical region. Scanclassifier data can also include originating entity data 423, indicatingthe hospital where the scan was taken and/or a user that uploaded thescan to the system. If the originating entity data corresponds to a userof one or more subsystems 101, the originating entity data can include acorresponding user profile identifier and/or include other data from theuser profile entry 354 of the user. Scan classifier data 420 can includegeographic region data 424, indicating a city, state, and/or countryfrom which the scan originated, for example, based on the user dataretrieved from the user database 344 based on the originating entity.Scan classifier data can also include machine data 425, which caninclude machine identifier data, machine model data, machine calibrationdata, and/or contrast agent data, for example based on imaging machinedata retrieved from the user database 344 based on the originatingentity data 423. The scan classifier data 420 can include scan date data426 indicating when the scan was taken. The scan classifier data 420 caninclude scan priority data 427, which can indicate a priority score,ranking, number in a queue, or other priority data with regard totriaging and/or review. A priority score, ranking, or queue number ofthe scan priority data 427 can be generated by automatically by asubsystem based on the scan priority data 427, based on a severity ofpatient symptoms or other indicators in the risk factor data 432, basedon a priority corresponding to the originating entity, based onpreviously generated diagnosis data 440 for the scan, and/or can beassigned by the originating entity and/or a user of the system.

The scan classifier data 420 can include other classifying data notpictured in FIG. 4A. For example, a set of scans can include medicalscan image data 410 corresponding to different imaging planes. The scanclassifier data can further include imaging plane data indicating one ormore imaging planes corresponding to the image data. For example, theimaging plane data can indicate the scan corresponds to the axial plane,sagittal plane, or coronal plane. A single medical scan entry 352 caninclude medical scan image data 410 corresponding multiple planes, andeach of these planes can be tagged appropriately in the image data. Inother embodiments, medical scan image data 410 corresponding to eachplane can be stored as separate medical scan entries 352, for example,with a common identifier indicating these entries belong to the same setof scans.

Alternatively or in addition, the scan classifier data 420 can includesequencing data. For example, a set of scans can include medical scanimage data 410 corresponding to different sequences. The scan classifierdata can further include sequencing data indicating one or more of aplurality of sequences of the image data corresponds to, for example,indicating whether an MRI scan corresponds to a T2 sequence, a T1sequence, a T1 sequence with contrast, a diffusion sequence, a FLAIRsequence, or other MM sequence. A single medical scan entry 352 caninclude medical scan image data 410 corresponding to multiple sequences,and each of these sequences can be tagged appropriately in the entry. Inother embodiments, medical scan image data 410 corresponding to eachsequence can be stored as separate medical scan entries 352, forexample, with a common identifier indicating these entries belong to thesame set of scans.

Alternatively or in addition, the scan classifier data 420 can includean image quality score. This score can be determined automatically byone or more subsystems 101, and/or can be manually assigned the medicalscan. The image quality score can be based on a resolution of the imagedata 410, where higher resolution image data is assigned a morefavorable image quality score than lower resolution image data. Theimage quality score can be based on whether the image data 410corresponds to digitized image data received directly from thecorresponding imaging machine, or corresponds to a hard copy of theimage data that was later scanned in. In some embodiments, the imagequality score can be based on a detected corruption, and/or detectedexternal factor that determined to negatively affect the quality of theimage data during the capturing of the medical scan and/or subsequent tothe capturing of the medical scan. In some embodiments, the imagequality score can be based on detected noise in the image data, where amedical scan with a higher level of detected noise can receive a lessfavorable image quality score than a medical scan with a lower level ofdetected noise. Medical scans with this determined corruption orexternal factor can receive a less favorable image quality score thanmedical scans with no detected corruption or external factor.

In some embodiments, the image quality score can be based on includemachine data 425. In some embodiments, one or more subsystems canutilize the image quality score to flag medical scans with image qualityscores that fall below an image quality threshold. The image qualitythreshold can be the same or different for different subsystems, medicalscan modalities, and/or anatomical regions. For example, the medicalscan image analysis system can automatically filter training sets basedon selecting only medical scans with image quality scores that comparefavorably to the image quality threshold. As another example, one ormore subsystems can flag a particular imaging machine and/or hospital orother medical entity that have produced at least a threshold numberand/or percentage of medical scan with image quality scores that compareunfavorably to the image quality threshold. As another example, ade-noising algorithm can be automatically utilized to clean the imagedata when the image quality score compares unfavorably to the imagequality threshold. As another example; the medical scan image analysissystem can select a particular medical image analysis function from aset of medical image analysis functions to utilize on a medical scan togenerate inference data for the medical scan. Each of this set ofmedical image analysis function can be trained on different levels ofimage quality, and the selected image analysis function can be selectedbased on the determined image quality score falling within a range ofimage quality scores the image analysis function was trained on and/oris otherwise suitable for.

The patient history data 430 can include patient identifier data 431which can include basic patient information such as name or anidentifier that may be anonymized to protect the confidentiality of thepatient, age, and/or gender. The patient identifier data 431 can alsomap to a patient entry in a separate patient database stored by thedatabase storage system, or stored elsewhere. The patient history datacan include patient risk factor data 432 which can include previousmedical history, family medical history, smoking and/or drug habits,pack years corresponding to tobacco use, environmental exposures,patient symptoms, etc. The patient history data 430 can also includelongitudinal data 433, which can identify one or more additional medicalscans corresponding to the patient, for example, retrieved based onpatient identifier data 431 or otherwise mapped to the patientidentifier data 431. Some or all additional medical scans can beincluded in the medical scan database, and can be identified based ontheir corresponding identifiers medical scan identifiers 353. Some orall additional medical scans can be received from a different source andcan otherwise be identified. Alternatively or in addition, thelongitudinal data can simply include some or all relevant scan entrydata of a medical scan entry 352 corresponding to the one or moreadditional medical scans. The additional medical scans can be the sametype of scan or different types of scans. Some or all of the additionalscans may correspond to past medical scans, and/or some or all of theadditional scans may correspond to future medical scans. Thelongitudinal data 433 can also include data received and/or determinedat a date after the scan such as final biopsy data, or some or all ofthe diagnosis data 440. The patient history data can also include alongitudinal quality score 434, which can be calculated automatically bya subsystem, for example, based on the number of additional medicalscans, based on how many of the additional scans in the file were takenbefore and/or after the scan based on the scan date data 426 of themedical scan and the additional medical scans, based on a date rangecorresponding to the earliest scan and corresponding to the latest scan,based on the scan types data 421 these scans, and/or based on whether ornot a biopsy or other final data is included. As used herein, a “high”longitudinal quality score refers to a scan having more favorablelongitudinal data than that with a “low” longitudinal quality score.

Diagnosis data 440 can include data that indicates an automateddiagnosis, a tentative diagnosis, and/or data that can otherwise be usedto support medical diagnosis, triage, medical evaluation and/or otherreview by a medical professional or other user. The diagnosis data 440of a medical scan can include a binary abnormality identifier 441indicating whether the scan is normal or includes at least oneabnormality. In some embodiments, the binary abnormality identifier 441can be determined by comparing some or all of confidence score data 460to a threshold, can be determined by comparing a probability value to athreshold, and/or can be determined by comparing another continuous ordiscrete value indicating a calculated likelihood that the scan containsone or more abnormalities to a threshold. In some embodiments,non-binary values, such as one or more continuous or discrete valuesindicating a likelihood that the scan contains one or moreabnormalities, can be included in diagnosis data 440 in addition to, orinstead of, binary abnormality identifier 441. One or abnormalities canbe identified by the diagnosis data 440, and each identified abnormalitycan include its own set of abnormality annotation data 442.Alternatively, some or all of the diagnosis data 440 can indicate and/ordescribe multiple abnormalities, and thus will not be presented for eachabnormality in the abnormality annotation data 442. For example, thereport data 449 of the diagnosis data 440 can describe all identifiedabnormalities, and thus a single report can be included in thediagnosis.

FIG. 4B presents an embodiment of the abnormality annotation data 442.The abnormality annotation data 442 for each abnormality can includeabnormality location data 443, which can include an anatomical locationand/or a location specific to pixels, image slices, coordinates or otherlocation information identifying regions of the medical scan itself. Theabnormality annotation data 442 can include abnormality classificationdata 445 which can include binary, quantitative, and/or descriptive dataof the abnormality as a whole, or can correspond to one or moreabnormality classifier categories 444, which can include size, volume,pre-post contrast, doubling time, calcification, components, smoothness,spiculation, lobulation, sphericity, internal structure, texture, orother categories that can classify and/or otherwise characterize anabnormality. Abnormality classifier categories 444 can be assigned abinary value, indicating whether or not such a category is present. Forexample, this binary value can be determined by comparing some or all ofconfidence score data 460 to a threshold, can be determined by comparinga probability value to a threshold, and/or can be determined bycomparing another continuous or discrete value indicating a calculatedlikelihood that a corresponding abnormality classifier category 444 ispresent to a threshold, which can be the same or different threshold foreach abnormality classifier category 444. In some embodiments,abnormality classifier categories 444 can be assigned one or morenon-binary values, such as one or more continuous or discrete valuesindicating a likelihood that the corresponding classifier category 444is present.

The abnormality classifier categories 444 can also include a malignancycategory, and the abnormality classification data 445 can include amalignancy rating such as a Lung-RADS score, a Fleischner score, and/orone or more calculated values that indicate malignancy level, malignancyseverity, and/or probability of malignancy. Alternatively or inaddition, the malignancy category can be assigned a value of “yes”,“no”, or “maybe”. The abnormality classifier categories 444 can alsoinclude abnormality pattern categories 446 such as cardiomegaly,consolidation, effusion, emphysema, and/or fracture, and the abnormalityclassification data 445 for each abnormality pattern category 446 canindicate whether or not each of the abnormality patterns is present.

The abnormality classifier categories can correspond to ResponseEvaluation Criteria in Solid Tumors (RECIST) eligibility and/or RECISTevaluation categories. For example, an abnormality classifier category444 corresponding to RECIST eligibility can have correspondingabnormality classification data 445 indicating a binary value “yes” or“no”, and/or can indicate if the abnormality is a “target lesion” and/ora “non-target lesion.” As another example, an abnormality classifiercategory 444 corresponding to a RECIST evaluation category can bedetermined based on longitudinal data 433 and can have correspondingabnormality classification data 445 that includes one of the set ofpossible values “Complete Response”, “Partial Response”, “StableDisease”, or “Progressive Disease.”

The diagnosis data 440 as a whole, and/or the abnormality annotationdata 442 for each abnormality, can include custom codes or datatypesidentifying the binary abnormality identifier 441, abnormality locationdata 443 and/or some or all of the abnormality classification data 445of one or more abnormality classifier categories 444. Alternatively orin addition, some or all of the abnormality annotation data 442 for eachabnormality and/or other diagnosis data 440 can be presented in a DICOMformat or other standardized image annotation format, and/or can beextracted into custom datatypes based on abnormality annotation dataoriginally presented in DICOM format. Alternatively or in addition, thediagnosis data 440 and/or the abnormality annotation data 442 for eachabnormality can be presented as one or more medical codes 447 such asSNOMED codes, Current Procedure Technology (CPT) codes, ICD-9 codes,ICD-10 codes, or other standardized medical codes used to label orotherwise describe medical scans.

Alternatively or in addition, the diagnosis data 440 can include naturallanguage text data 448 annotating or otherwise describing the medicalscan as a whole, and/or the abnormality annotation data 442 can includenatural language text data 448 annotating or otherwise describing eachcorresponding abnormality. In some embodiments, some or all of thediagnosis data 440 is presented only as natural language text data 448.In some embodiments, some or all of the diagnosis data 440 isautomatically generated by one or more subsystems based on the naturallanguage text data 448, for example, without utilizing the medical scanimage data 410, for example, by utilizing one or more medical scannatural language analysis functions trained by the medical scan naturallanguage analysis system 114. Alternatively or in addition, someembodiments, some or all of the natural language text data 448 isgenerated automatically based on other diagnosis data 440 such asabnormality annotation data 442, for example, by utilizing a medicalscan natural language generating function trained by the medical scannatural language analysis system 114.

The diagnosis data can include report data 449 that includes at leastone medical report, which can be formatted to include some or all of themedical codes 447, some or all of the natural language text data 448,other diagnosis data 440, full or cropped images slices formatted basedon the display parameter data 470 and/or links thereto, full or croppedimages slices or other data based on similar scans of the similar scandata 480 and/or links thereto, full or cropped images or other databased on patient history data 430 such as longitudinal data 433 and/orlinks thereto, and/or other data or links to data describing the medicalscan and associated abnormalities. The diagnosis data 440 can alsoinclude finalized diagnosis data corresponding to future scans and/orfuture diagnosis for the patient, for example, biopsy data or otherlongitudinal data 433 determined subsequently after the scan. Themedical report of report data 449 can be formatted based on specifiedformatting parameters such as font, text size, header data, bulleting ornumbering type, margins, file type, preferences for including one ormore full or cropped image slices 412, preferences for including similarmedical scans, preferences for including additional medical scans, orother formatting to list natural language text data and/or image data,for example, based on preferences of a user indicated in the originatingentity data 423 or other responsible user in the corresponding reportformatting data.

Annotation author data 450 can be mapped to the diagnosis data for eachabnormality, and/or mapped to the scan as a whole. This can include oneor more annotation author identifiers 451, which can include one or moreuser profile identifiers of a user of the system, such as an individualmedical professional, medical facility and/or medical entity that usesthe system. Annotation author data 450 can be used to determine theusage data of a user profile entry 354. Annotation author data 450 canalso include one or more medical scan analysis function identifiers 357or other function identifier indicating one or more functions or otherprocesses of a subsystem responsible for automatically generating and/orassisting a user in generating some or all of the diagnosis data, forexample an identifier of a particular type and/or version of a medicalscan image analysis functions that was used by the medical scandiagnosing system 108 used to generate part or all of the diagnosis data440 and/or an interface feature identifier, indicating an one or moreinterface features presented to a user to facilitate entry of and/orreviewing of the diagnosis data 440. The annotation author data can alsosimply indicate, for one or more portions of the diagnosis data 440, ifthis portion was generated by a human or automatically generated by asubsystem of the medical scan processing system.

In some embodiments, if a medical scan was reviewed by multipleentities, multiple, separate diagnosis data entries 440 can be includedin the medical scan entry 352, mapped to each diagnosis author in theannotation author data 450. This allows different versions of diagnosisdata 440 received from multiple entities. For example, annotation authordata of a particular medical scan could indicate that the annotationdata was written by a doctor at medical entity A, and the medical codedata was generated by user Y by utilizing the medical scan reportlabeling system 104, which was confirmed by expert user X. Theannotation author data of another medical scan could indicate that themedical code was generated automatically by utilizing version 7 of themedical scan image analysis function relating to chest x-rays, andconfirmed by expert user X. The annotation author data of anothermedical scan could indicate that the location and a first malignancyrating were generated automatically by utilizing version 7 of themedical scan image analysis function relating to chest x-rays, and thata second malignancy rating was entered by user Z. In some embodiments,one of the multiple diagnosis entries can include consensus annotationdata, for example, generated automatically by a subsystem such as themedical scan annotating system 106 based on the multiple diagnosis data440, based on confidence score data 460 of each of the multiplediagnosis data 440, and/or based on performance score data of acorresponding user, a medical scan analysis function, or an interfacefeature, identified in the annotation author data for each correspondingone of the multiple diagnosis data 440.

Confidence score data 460 can be mapped to some or all of the diagnosisdata 440 for each abnormality, and/or for the scan as a whole. This caninclude an overall confidence score for the diagnosis, a confidencescore for the binary indicator of whether or not the scan was normal, aconfidence score for the location a detected abnormality, and/orconfidence scores for some or all of the abnormality classifier data.This may be generated automatically by a subsystem, for example, basedon the annotation author data and corresponding performance score of oneor more identified users and/or subsystem attributes such as interactiveinterface types or medical scan image analysis functions indicated bythe annotation author data. In the case where multiple diagnosis dataentries 440 are included from different sources, confidence score data460 can be computed for each entry and/or an overall confidence score,for example, corresponding to consensus diagnosis data, can be based oncalculated distance or other error and/or discrepancies between theentries, and/or can be weighted on the confidence score data 460 of eachentry. In various embodiments, the confidence score data 460 can includea truth flag 461 indicating the diagnosis data is considered as “known”or “truth”, for example, flagged based on user input, flaggedautomatically based on the author data, and/or flagged automaticallybased on the calculated confidence score of the confidence score dataexceeding a truth threshold. As used herein, a “high” confidence scorerefers to a greater degree or more favorable level of confidence than a“low” confidence score.

Display parameter data 470 can indicate parameters indicating an optimalor preferred display of the medical scan by an interactive interface 275and/or formatted report for each abnormality and/or for the scan as awhole. Some or all of the display parameter data can have separateentries for each abnormality, for example, generated automatically by asubsystem 101 based on the abnormality annotation data 442. Displayparameter data 470 can include interactive interface feature data 471,which can indicate one or more selected interface features associatedwith the display of abnormalities and/or display of the medical scan asa whole, and/or selected interface features associated with userinteraction with a medical scan, for example, based on categorizedinterface feature performance score data and a category associated withthe abnormality and/or with the medical scan itself. The displayparameter data can include a slice subset 472, which can indicate aselected subset of the plurality of image slices that includes a singleimage slice 412 or multiple image slices 412 of the medical scan imagedata 410 for display by a user interface. The display parameter data 470can include slice order data 473 that indicates a selected customordering and/or ranking for the slice subset 472, or for all of theslices 412 of the medical scan. The display parameter data 470 caninclude slice cropping data 474 corresponding to some or all of theslice subset 472, or all of the image slices 412 of the medical scan,and can indicating a selected custom cropped region of each image slice412 for display, or the same selected custom cropped region for theslice subset 472 or for all slices 412. The display parameter data caninclude density window data 475, which can indicate a selected customdensity window for display of the medical scan as a whole, a selectedcustom density window for the slices subset 472, and/or selected customdensity windows for each of the image slices 412 of the slice subset472, and/or for each image slice 412 of the medical scan. The densitywindow data 475 can indicate a selected upper density value cut off anda selected lower density value cut off, and/or can include a selecteddeterministic function to map each density value of a pixel to agrayscale value based on the preferred density window. The interactiveinterface feature data 471, slice subset 472, slice order data 473,slice cropping data 474, and/or the density window data 475 can beselected via user input and/or generated automatically by one or moresubsystems 101, for example, based on the abnormality annotation data442 and/or based on performance score data of different interactiveinterface versions.

Similar scan data 480 can be mapped to each abnormality, or the scan asa whole, and can include similar scan identifier data 481 correspondingto one or more identified similar medical scans, for example,automatically identified by a subsystem 101, for example, by applying asimilar scan identification step of the medical scan image analysissystem 112 and/or applying medical scan similarity analysis function tosome or all of the data stored in the medical scan entry of the medicalscan, and/or to some or all corresponding data of other medical scans inthe medical scan database. The similar scan data 480 can also correspondto medical scans received from another source. The stored similaritydata can be used to present similar cases to users of the system and/orcan be used to train medical scan image analysis functions or medicalscan similarity analysis functions.

Each identified similar medical scan can have its own medical scan entry352 in the medical scan database 342 with its own data, and the similarscan identifier data 481 can include the medical scan identifier 353each similar medical scan. Each identified similar medical scan can be ascan of the same scan type or different scan type than medical scan.

The similar scan data 480 can include a similarity score 482 for eachidentified similar scan, for example, generated based on some or all ofthe data of the medical scan entry 352 for medical scan and based onsome or all of the corresponding data of the medical scan entry 352 forthe identified similar medical scan. For example, the similarity score482 can be generated based on applying a medical scan similarityanalysis function to the medical image scan data of medical scans and402, to some or all of the abnormality annotation data of medical scansand 402, and/or to some or all of the patient history data 430 ofmedical scans and 402 such as risk factor data 432. As used herein, a“high” similarity score refers a higher level of similarity that a “low”similarity score.

The similar scan data 480 can include its own similar scan displayparameter data 483, which can be determined based on some or all of thedisplay parameter data 470 of the identified similar medical scan. Someor all of the similar scan display parameter data 483 can be generatedautomatically by a subsystem, for example, based on the displayparameter data 470 of the identified similar medical scan, based on theabnormality annotation data 442 of the medical scan itself and/or basedon display parameter data 470 of the medical scan itself. Thus, thesimilar scan display parameter data 483 can be the same or differentthan the display parameter data 470 mapped to the identified similarmedical scan and/or can be the same or different than the displayparameter data 470 of the medical scan itself. This can be utilized whendisplaying similar scans to a user via interactive interface 275 and/orcan be utilized when generating report data 449 that includes similarscans, for example, in conjunction with the medical scan assisted reviewsystem 102.

The similar scan data 480 can include similar scan abnormality data 484,which can indicate one of a plurality of abnormalities of the identifiedsimilar medical scan and its corresponding abnormality annotation data442. For example, the similarity scan abnormality data 484 can includean abnormality pair that indicates one of a plurality of abnormalitiesof the medical scan, and indicates one of a plurality of abnormalitiesof the identified similar medical scan, for example, that was identifiedas the similar abnormality.

The similar scan data 480 can include similar scan filter data 485. Thesimilar scan filter data can be generated automatically by a subsystem,and can include a selected ordered or un-ordered subset of allidentified similar scans of the similar scan data 480, and/or a rankingof all identified similar scans. For example, the subset can be selectedand/or some or all identified similar scans can be ranked based on eachsimilarity score 482, and/or based on other factors such as based on alongitudinal quality score 434 of each identified similar medical scan.

The training set data 490 can indicate one or more training sets thatthe medical scan belongs to. For example, the training set data canindicate one or more training set identifiers 491 indicating one or moremedical scan analysis functions that utilized the medical scan in theirtraining set, and/or indicating a particular version identifier 641 ofthe one or more medical scan analysis functions that utilized themedical scan in their training set. The training set data 490 can alsoindicate which portions of the medical scan entry were utilized by thetraining set, for example, based on model parameter data 623 of thecorresponding medical scan analysis functions. For example, the trainingset data 490 can indicate that the medical scan image data 410 wasincluded in the training set utilized to train version X of the chestx-ray medical scan image analysis function, or that the natural languagetext data 448 of this medical scan was used to train version Y of thenatural language analysis function.

FIG. 5A presents an embodiment of a user profile entry 354, stored inuser database 344 or otherwise associated with a user. A user cancorrespond to a user of one or more of the subsystems such as aradiologist, doctor, medical professional, medical report labeler,administrator of one or more subsystems or databases, or other user thatuses one or more subsystems 101. A user can also correspond to a medicalentity such as a hospital, medical clinic, establishment that utilizesmedical scans, establishment that employs one or more of the medicalprofessionals described, an establishment associated with administeringone or more subsystems, or other entity. A user can also correspond to aparticular client device 120 or account that can be accessed one or moremedical professionals or other employees at the same or differentmedical entities. Each user profile entry can have a corresponding userprofile identifier 355.

A user profile entry 354 can include basic user data 510, which caninclude identifying information 511 corresponding to the user such as aname, contact information, account/login/password information,geographic location information such as geographic region data 424,and/or other basic information. Basic user data 510 can includeaffiliation data 512, which can list one or more medical entities orother establishments the user is affiliated with, for example, if theuser corresponds to a single person such as a medical professional, orif the user corresponds to a hospital in a network of hospitals. Theaffiliation data 512 can include one or more corresponding user profileidentifiers 355 and/or basic user data 510 if the correspondingaffiliated medical entity or other establishment has its own entry inthe user database. The user identifier data can include employee data513 listing one or more employees, such as medical professionals withtheir own user profile entries 354, for example, if the user correspondsto a medical entity or supervising medical professional of other medicalprofessional employees, and can list a user profile identifier 355and/or basic user data 510 for each employee. The basic user data 510can also include imaging machine data 514, which can include a list ofmachines affiliated with the user which can include machine identifiers,model information, calibration information, scan type information, orother data corresponding to each machine, for example, corresponding tothe machine data 425. The user profile entry can include client devicedata 515, which can include identifiers for one or more client devicesassociated with the user, for example, allowing subsystems 101 to senddata to a client device 120 corresponding to a selected user based onthe client device data and/or to determine a user that data was receivedby determining the client device from which the data was received.

The user profile entry can include usage data 520 which can includeidentifying information for a plurality of usages by the user inconjunction with using one or more subsystems 101. This can includeconsumption usage data 521, which can include a listing of, or aggregatedata associated with, usages of one or more subsystems by the user, forexample, where the user is utilizing the subsystem as a service. Forexample, the consumption usage data 521 can correspond to each instancewhere diagnosis data was sent to the user for medical scans provided tothe user in conjunction with the medical scan diagnosing system 108and/or the medical scan assisted review system 102. Some or all ofconsumption usage data 521 can include training usage data 522,corresponding to usage in conjunction with a certification program orother user training provided by one or more subsystems. The trainingusage data 522 can correspond to each instance where diagnosis feedbackdata was provided by user for a medical scan with known diagnosis data,but diagnosis feedback data is not utilized by a subsystem to generate,edit, and/or confirm diagnosis data 440 of the medical scan, as it isinstead utilized to train a user and/or determine performance data for auser.

Usage data 520 can include contribution usage data 523, which caninclude a listing of, or aggregate data associated with, usages of oneor more subsystems 101 by the user, for example, where the user isgenerating and/or otherwise providing data and/or feedback that can isutilized by the subsystems, for example, to generate, edit, and/orconfirm diagnosis data 440 and/or to otherwise populate, modify, orconfirm portions of the medical scan database or other subsystem data.For example, the contribution usage data 523 can correspond to diagnosisfeedback data received from user, used to generate, edit, and/or confirmdiagnosis data. The contribution usage data 523 can include interactiveinterface feature data 524 corresponding to the interactive interfacefeatures utilized with respect to the contribution.

The consumption usage data 521 and/or the contribution usage data 523can include medical scan entry 352 whose entries the user utilizedand/or contributed to, can indicate one or more specific attributes of amedical scan entry 352 that a user utilized and/or contributed to,and/or a log of the user input generated by a client device of the userin conjunction with the data usage. The contribution usage data 523 caninclude the diagnosis data that the user may have generated and/orreviewed, for example, indicated by, mapped to, and/or used to generatethe annotation author data 450 of corresponding medical scan entries352. Some usages may correspond to both consumption usage of theconsumption usage data 521 and contribution usage of the contributionusage data 523. The usage data 520 can also indicate one or moresubsystems 101 that correspond to each consumption and/or contribution.

The user profile entry can include performance score data 530. This caninclude one or more performance scores generated based on thecontribution usage data 523 and/or training usage data 522. Theperformance scores can include separate performance scores generated forevery contribution in the contribution usage data 523 and/or trainingusage data 522 and/or generated for every training consumption usagescorresponding to a training program. As used herein, a “high”performance score refers to a more favorable performance or rating thana “low” performance score.

The performance score data can include accuracy score data 531, whichcan be generated automatically by a subsystem for each contribution, forexample, based on comparing diagnosis data received from a user to datato known truth data such as medical scans with a truth flag 461, forexample, retrieved from the corresponding medical scan entry 352 and/orbased on other data corresponding to the medical scan, for example,received from an expert user that later reviewed the contribution usagedata of the user and/or generated automatically by a subsystem. Theaccuracy score data 531 can include an aggregate accuracy scoregenerated automatically by a subsystem, for example, based on theaccuracy data of multiple contributions by the user over time.

The performance data can also include efficiency score data 532generated automatically by a subsystem for each contribution based on anamount of time taken to complete a contribution, for example, from atime the request for a contribution was sent to the client device to atime that the contribution was received from the client device, based ontiming data received from the client device itself, and/or based onother factors. The efficiency score can include an aggregate efficiencyscore, which can be generated automatically by a subsystem based on theindividual efficiency scores over time and/or based on determining acontribution completion rate, for example based on determining how manycontributions were completed in a fixed time window.

Aggregate performance score data 533 can be generated automatically by asubsystem based on the aggregate efficiency and/or accuracy data. Theaggregate performance data can include categorized performance data 534,for example, corresponding to different scan types, different anatomicalregions, different subsystems, different interactive interface featuresand/or display parameters. The categorized performance data 534 can bedetermined automatically by a subsystem based on the scan type data 421and/or anatomical region data 422 of the medical scan associated witheach contribution, one or more subsystems 101 associated with eachcontribution, and/or interactive interface feature data 524 associatedwith each contribution. The aggregate performance data can also be basedon performance score data 530 of individual employees if the usercorresponds to a medical entity, for example, retrieved based on userprofile identifiers 355 included in the employee data 513. Theperformance score data can also include ranking data 535, which caninclude an overall ranking or categorized rankings, for example,generated automatically by a subsystem or the database itself based onthe aggregate performance data.

In some embodiments, aggregate data for each user can be further brokendown based on scores for distinct scan categories, for example, based onthe scan classifier data 420, for example, where a first aggregate datascore is generated for a user “A” based on scores from all knee x-rays,and a second aggregate data score is generated for user A based onscores from all chest CT scans. Aggregate data for each user can befurther based on scores for distinct diagnosis categories, where a firstaggregate data score is generated for user A based on scores from allnormal scans, and a second aggregate data score is generated for user Abased on scores from all scans that contain an abnormality. This can befurther broken down, where a first aggregate score is generated for userA based on all scores from scans that contain an abnormality of a firsttype and/or in a first anatomical location, and a second aggregate scoreis generated for A based on all scores from scans that contain anabnormality of a second type and/or in a second location. Aggregate datafor each user can be further based on affiliation data, where a rankingis generated for a medical professional “B” based on scores from allmedical professionals with the same affiliation data, and/or where aranking is generated for a hospital “C” based on scores for allhospitals, all hospitals in the same geographical region, etc. Aggregatedata for each user can be further based on scores for interfacefeatures, where a first aggregate data score is generated for user Abased on scores using a first interface feature, and a second aggregatedata score is generated for user A based on scores using a firstinterface feature.

The user profile entry can include qualification data 540. Thequalification data can include experience data 541 such as educationdata, professional practice data, number of years practicing, awardsreceived, etc. The qualification data 540 can also include certificationdata 542 corresponding to certifications earned based on contributionsto one or more subsystems, for example, assigned to users automaticallyby a subsystem based on the performance score data 530 and/or based on anumber of contributions in the contribution usage data 523 and/ortraining usage data 522. For example, the certifications can correspondto standard and/or recognized certifications to train medicalprofessionals and/or incentivize medical professionals to use thesystem. The qualification data 540 can include expert data 543. Theexpert data 543 can include a binary expert identifier, which can begenerated automatically by a subsystem based on experience data 541,certification data 542, and/or the performance score data 530, and canindicate whether the user is an expert user. The expert data 543 caninclude a plurality of categorized binary expert identifierscorresponding to a plurality of qualification categories correspondingto corresponding to scan types, anatomical regions, and/or theparticular subsystems. The categorized binary expert identifiers can begenerated automatically by a subsystem based on the categorizedperformance data 534 and/or the experience data 541. The categories beranked by performance score in each category to indicate particularspecialties. The expert data 543 can also include an expert ranking orcategorized expert ranking with respect to all experts in the system.

The user profile entry can include subscription data 550, which caninclude a selected one of a plurality of subscription options that theuser has subscribed to. For example, the subscription options cancorrespond to allowed usage of one or more subsystems, such as a numberof times a user can utilize a subsystem in a month, and/or to acertification program, for example paid for by a user to receivetraining to earn a subsystem certification of certification data 542.The subscription data can include subscription expiration information,and/or billing information. The subscription data can also includesubscription status data 551, which can for example indicate a number ofremaining usages of a system and/or available credit information. Forexample, the remaining number of usages can decrease and/or availablecredit can decrease in response to usages that utilize one or moresubsystems as a service, for example, indicated in the consumption usagedata 521 and/or training usage data 522. In some embodiments, theremaining number of usages can increase and/or available credit canincrease in response to usages that correspond to contributions, forexample, based on the contribution usage data 523. An increase in creditcan be variable, and can be based on a determined quality of eachcontribution, for example, based on the performance score data 530corresponding to the contribution where a higher performance scorecorresponds to a higher increase in credit, based on scan priority data427 of the medical scan where contributing to higher priority scanscorresponds to a higher increase in credit, or based on other factors.

The user profile entry 354 can include interface preference data 560.The interface preference data can include a preferred interactiveinterface feature set 561, which can include one or more interactiveinterface feature identifiers and/or one or more interactive interfaceversion identifiers of interface feature entries 358 and/or versionidentifiers of the interface features. Some or all of the interfacefeatures of the preferred interactive interface feature set 561 cancorrespond to display parameter data 470 of medical scans. The preferredinteractive interface feature set 561 can include a single interactivefeature identifier for one or more feature types and/or interface types,and/or can include a single interactive interface version identifier forone or more interface categories. The preferred interactive interfacefeature set 561 can include a ranking of multiple features for the samefeature type and/or interface type. The ranked and/or unranked preferredinteractive interface feature set 561 can be generated based on userinput to an interactive interface of the client device to select and/orrank some or all of the interface features and/or versions. Some or allof the features and/or versions of the preferred interactive feature setcan be selected and/or ranked automatically by a subsystem such as themedical scan interface evaluator system, for example based on interfacefeature performance score data and/or feature popularity data.Alternatively or in addition, the performance score data 530 can beutilized by a subsystem to automatically determine the preferredinteractive feature set, for example, based on the scores in differentfeature-based categories of the categorized performance data 534.

The user profile entry 354 can include report formatting data 570, whichcan indicate report formatting preferences indicated by the user. Thiscan include font, text size, header data, bulleting or numbering type,margins, file type, preferences for including one or more full orcropped image slices 412, preferences for including similar medicalscans, preferences for including additional medical scans in reports, orother formatting preference to list natural language text data and/orimage data corresponding to each abnormality. Some or all of the reportformatting data 570 can be based on interface preference data 560. Thereport formatting data 570 can be used by one or more subsystems toautomatically generate report data 449 of medical scans based on thepreferences of the requesting user.

FIG. 5B presents an embodiment of a medical scan analysis function entry356, stored in medical scan analysis function database 346 or otherwiseassociated with one of a plurality of medical scan analysis functionstrained by and/or utilized by one or more subsystems 101. For example, amedical scan analysis function can include one or more medical scanimage analysis functions trained by the medical scan image analysissystem 112; one or more medical scan natural language analysis functionstrained by the medical scan natural language analysis system 114; one ormore medical scan similarity analysis function trained by the medicalscan image analysis system 112, the medical scan natural languageanalysis system 114, and/or the medical scan comparison system 116; oneor more medical report generator functions trained by the medical scannatural language analysis system 114 and/or the medical scan imageanalysis system 112, and/or the medical report analysis function trainedby the medical scan natural language analysis system 114. Some or all ofthe medical scan analysis functions can correspond to medical scaninference functions of the medical scan diagnosing system 108, thede-identification function and/or the inference functions utilized by amedical picture archive integration system as discussed in conjunctionwith FIGS. 8A-8F, or other functions and/or processes described hereinin conjunction with one or more subsystems 101. Each medical scananalysis function entry 356 can include a medical scan analysis functionidentifier 357.

A medical scan analysis function entry 356 can include functionclassifier data 610. Function classifier data 610 can include input andoutput types corresponding to the function. For example the functionclassifier data can include input scan category 611 that indicates whichtypes of scans can be used as input to the medical scan analysisfunction. For example, input scan category 611 can indicate that amedical scan analysis function is for chest CT scans from a particularhospital or other medical entity. The input scan category 611 caninclude one or more categories included in scan classifier data 420. Invarious embodiments, the input scan category 611 corresponds to thetypes of medical scans that were used to train the medical scan analysisfunction. Function classifier data 610 can also include output type data612 that characterizes the type of output that will be produced by thefunction, for example, indicating that a medical scan analysis functionis used to generate medical codes 447. The input scan category 611 canalso include information identifying which subsystems 101 areresponsible for running the medical scan analysis function.

A medical scan analysis function entry 356 can include trainingparameters 620. This can include training set data 621, which caninclude identifiers for the data used to train the medical scan analysisfunction, such as a set of medical scan identifiers 353 corresponding tothe medical scans used to train the medical scan analysis function, alist of medical scan reports and corresponding medical codes used totrain the medical scan analysis function, etc. Alternatively or inaddition to identifying particular scans of the training set, thetraining set data 621 can identify training set criteria, such asnecessary scan classifier data 420, necessary abnormality locations,classifiers, or other criteria corresponding to abnormality annotationdata 442, necessary confidence score data 460, for example, indicatingthat only medical scans with diagnosis data 440 assigned a truth flag461 or with confidence score data 460 otherwise comparing favorably to atraining set confidence score threshold are included, a number ofmedical scans to be included and proportion data corresponding todifferent criteria, or other criteria used to populate a training setwith data of medical scans. Training parameters 620 can include modeltype data 622 indicating one or more types of model, methods, and/ortraining functions used to determine the medical scan analysis functionby utilizing the training set 621. Training parameters 620 can includemodel parameter data 623 that can include a set of features of thetraining data selected to train the medical scan analysis function,determined values for weights corresponding to selected input and outputfeatures, determined values for model parameters corresponding to themodel itself, etc. The training parameter data can also include testingdata 624, which can identify a test set of medical scans or other dataused to test the medical scan analysis function. The test set can be asubset of training set 621, include completely separate data thantraining set 621, and/or overlap with training set 621. Alternatively orin addition, testing data 624 can include validation parameters such asa percentage of data that will be randomly or pseudo-randomly selectedfrom the training set for testing, parameters characterizing a crossvalidation process, or other information regarding testing. Trainingparameters 620 can also include training error data 625 that indicates atraining error associated with the medical scan analysis function, forexample, based on applying cross validation indicated in testing data624.

A medical scan analysis function entry 356 can include performance scoredata 630. Performance data can include model accuracy data 631, forexample, generated and/or updated based on the accuracy of the functionwhen performed on new data. For example, the model accuracy data 631 caninclude or be calculated based on the model error for determined forindividual uses, for example, generated by comparing the output of themedical scan analysis function to corresponding data generated by userinput to interactive interface 275 in conjunction with a subsystem 101and/or generated by comparing the output of the medical scan analysisfunction to medical scans with a truth flag 461. The model accuracy data631 can include aggregate model accuracy data computed based on modelerror of individual uses of the function over time. The performancescore data 630 can also include model efficiency data 632, which can begenerated based on how quickly the medical scan analysis functionperforms, how much memory is utilized by medical scan analysis function,or other efficiency data relating to the medical scan analysis function.Some or all of the performance score data 630 can be based on trainingerror data 625 or other accuracy and/or efficiency data determinedduring training and/or validation. As used herein, a “high” performancescore refers to a more favorable performance or rating than a “low”performance score.

A medical scan analysis function entry 356 can include version data 640.The version data can include a version identifier 641. The version datacan indicate one or more previous version identifiers 642, which can mapto version identifiers 641 stored in other medical scan analysisfunction entry 356 that correspond to previous versions of the function.Alternatively or in addition, the version data can indicate multipleversions of the same type based on function classifier data 610, canindicate the corresponding order and/or rank of the versions, and/or canindicate training parameters 620 associated with each version.

A medical scan analysis function entry 356 can include remediation data650. Remediation data 650 can include remediation instruction data 651which can indicate the steps in a remediation process indicating how amedical scan analysis function is taken out of commission and/orreverted to a previous version in the case that remediation isnecessary. The version data 640 can further include remediation criteriadata 652, which can include threshold data or other criteria used toautomatically determine when remediation is necessary. For example, theremediation criteria data 652 can indicate that remediation is necessaryat any time where the model accuracy data and/or the model efficiencydata compares unfavorably to an indicated model accuracy thresholdand/or indicated model efficiency threshold. The remediation data 650can also include recommissioning instruction data 653, identifyingrequired criteria for recommissioning a medical scan analysis functionand/or updating a medical scan analysis function. The remediation data650 can also include remediation history, indicating one or moreinstances that the medical scan analysis function was taken out ofcommission and/or was recommissioned.

FIGS. 6A and 6B present an embodiment of a medical scan diagnosingsystem 108. The medical scan diagnosing system 108 can generateinference data 1110 for medical scans by utilizing a set of medical scaninference functions 1105, stored and run locally, stored and run byanother subsystem 101, and/or stored in the medical scan analysisfunction database 346, where the function and/or parameters of thefunction can be retrieved from the database by the medical scandiagnosing system. For example, the set of medical scan inferencefunction 1105 can include some or all medical scan analysis functionsdescribed herein or other functions that generate inference data 1110based on some or all data corresponding to a medical scan such as someor all data of a medical scan entry 352. Each medical scan inferencefunction 1105 in the set can correspond to a scan category 1120, and canbe trained on a set of medical scans that compare favorably to the scancategory 1120. For example, each inference function can be trained on aset of medical scans of the one or more same scan classifier data 420,such as the same and/or similar scan types, same and/or similaranatomical regions locations, same and/or similar machine models, sameand/or similar machine calibration, same and/or similar contrastingagent used, same and/or similar originating entity, same and/or similargeographical region, and/or other classifiers. Thus, the scan categories1120 can correspond to one or more of a scan type, scan anatomicalregion data, hospital or other originating entity data, machine modeldata, machine calibration data, contrast agent data, geographic regiondata, and/or other scan classifying data 420. For example, a firstmedical scan inference function can be directed to characterizing kneex-rays, and a second medical scan inference function can be directed tochest CT scans. As another example, a first medical scan inferencefunction can be directed to characterizing CT scans from a firsthospital, and a second medical scan image analysis function can bedirected to characterizing CT scans from a second hospital.

Training on these categorized sets separately can ensure each medicalscan inference function 1105 is calibrated according to its scancategory 1120, for example, allowing different inference functions to becalibrated on type specific, anatomical region specific, hospitalspecific, machine model specific, and/or region-specific tendenciesand/or discrepancies. Some or all of the medical scan inferencefunctions 1105 can be trained by the medical scan image analysis systemand/or the medical scan natural language processing system, and/or somemedical scan inference functions 1105 can utilize both image analysisand natural language analysis techniques to generate inference data1110. For example, some or all of the inference functions can utilizeimage analysis of the medical scan image data 410 and/or naturallanguage data extracted from abnormality annotation data 442 and/orreport data 449 as input, and generate diagnosis data 440 such asmedical codes 447 as output. Each medical scan inference function canutilize the same or different learning models to train on the same ordifferent features of the medical scan data, with the same or differentmodel parameters, for example indicated in the model type data 622 andmodel parameter data 623. Model type and/or parameters can be selectedfor a particular medical scan inference function based on particularcharacteristics of the one or more corresponding scan categories 1120,and some or all of the indicated in the model type data 622 and modelparameter data 623 can be selected automatically by a subsystem duringthe training process based on the particular learned and/or otherwisedetermined characteristics of the one or more corresponding scancategories 1120.

As shown in FIG. 6A, the medical scan diagnosing system 108 canautomatically select a medical scan for processing in response toreceiving it from a medical entity via the network. Alternatively, themedical scan diagnosing system 108 can automatically retrieve a medicalscan from the medical scan database that is selected based on a requestreceived from a user for a particular scan and/or based on a queue ofscans automatically ordered by the medical scan diagnosing system 108 oranother subsystem based on scan priority data 427.

Once a medical scan to be processed is determined, the medical scandiagnosing system 108 can automatically select an inference function1105 based on a determined scan category 1120 of the selected medicalscan and based on corresponding inference function scan categories. Thescan category 1120 of a scan can be determined based one some or all ofthe scan classifier data 420 and/or based on other metadata associatedwith the scan. This can include determining which one of the pluralityof medical scan inference functions 1105 matches or otherwise comparesfavorably to the scan category 1120, for example, by comparing the scancategory 1120 to the input scan category of the function classifier data610.

Alternatively or in addition, the medical scan diagnosing system 108 canautomatically determine which medical scan inference function 1105 isutilized based on an output preference that corresponding to a desiredtype of inference data 1110 that is outputted by an inference function1105. The output preference designated by a user of the medical scandiagnosing system 108 and/or based on the function of a subsystem 101utilizing the medical scan diagnosing system 108. For example, the setof inference functions 1105 can include inference functions that areutilized to indicate whether or not a medical scan is normal, toautomatically identify at least one abnormality in the scan, toautomatically characterize the at least one abnormality in the scan, toassign one or more medical codes to the scan, to generate naturallanguage text data and/or a formatted report for the scan, and/or toautomatically generate other diagnosis data such as some or all ofdiagnosis data 440 based on the medical scan. Alternatively or inaddition, some inference functions can also be utilized to automaticallygenerate confidence score data 460, display parameter data 470, and/orsimilar scan data 480. The medical scan diagnosing system 108 cancompare the output preference to the output type data 612 of the medicalscan inference function 1105 to determine the selected inferencefunction 1105. For example, this can be used to decide between a firstmedical scan inference function that automatically generates medicalcodes and a second medical scan inference function that automaticallygenerates natural language text for medical reports based on the desiredtype of inference data 1110.

Prior to performing the selected medical scan inference function 1105,the medical scan diagnosing system 108 can automatically perform aninput quality assurance function 1106 to ensure the scan classifier data420 or other metadata of the medical scan accurately classifies themedical scan such that the appropriate medical scan inference function1105 of the appropriate scan category 1120 is selected. The inputquality assurance function can be trained on, for example, medical scanimage data 410 of plurality of previous medical scans with verified scancategories. Thus, the input quality assurance function 1106 can takemedical scan image data 410 as input and can generate an inferred scancategory as output. The inferred scan category can be compared to thescan category 1120 of the scan, and the input quality assurance function1106 can determine whether or not the scan category 1120 is appropriateby determining whether the scan category 1120 compares favorably to theautomatically generated inferred scan category. The input qualityassurance function 1106 can also be utilized to reassign the generatedinferred scan category to the scan category 1120 when the scan category1120 compares favorably to the automatically generated inferred scancategory. The input quality assurance function 1106 can also be utilizedto assign the generated inferred scan category to the scan category 1120for incoming medical scans that do not include any classifying data,and/or to add classifiers in scan classifier data 420 to medical scansmissing one or more classifiers.

In various embodiments, upon utilizing the input quality assurancefunction 1106 to determine that the scan category 1120 determined by ascan classifier data 420 or other metadata is inaccurate, the medicalscan diagnosing system 108 can transmit an alert and/or an automaticallygenerated inferred scan category to the medical entity indicating thatthe scan is incorrectly classified in the scan classifier data 420 orother metadata. In some embodiments, the medical scan diagnosing system108 can automatically update performance score data corresponding to theoriginating entity of the scan indicated in originating entity data 423,or another user or entity responsible for classifying the scan, forexample, where a lower performance score is generated in response todetermining that the scan was incorrectly classified and/or where ahigher performance score is generated in response to determining thatthe scan was correctly classified.

In some embodiments, the medical scan diagnosing system 108 can transmitthe medical scan and/or the automatically generated inferred scancategory to a selected user. The user can be presented the medical scanimage data 410 and/or other data of the medical scan via the interactiveinterface 275, for example, displayed in conjunction with the medicalscan assisted review system 102. The interface can prompt the user toindicate the appropriate scan category 1120 and/or prompt the user toconfirm and/or edit the inferred scan category, also presented to theuser. For example, scan review data can be automatically generated toreflect the user generated and/or verified scan category 1120, This userindicated scan category 1120 can be utilized to select to the medicalscan inference function 1105 and/or to update the scan classifier data420 or other metadata accordingly. In some embodiments, for example,where the scan review data indicates that the selected user disagreeswith the automatically generated inferred scan category created by theinput quality assurance function 1106, the medical scan diagnosingsystem 108 can automatically update performance score data 630 of theinput quality assurance function 1106 by generating a low performancescore and/or determine to enter the remediation step 1140 for the inputquality assurance function 1106.

The medical scan diagnosing system 108 can also automatically perform anoutput quality assurance step after a medical scan inference function1105 has been performed on a medical scan to produce the inference data1110, as illustrated in the embodiment presented in FIG. 6B. The outputquality assurance step can be utilized to ensure that the selectedmedical scan inference function 1105 generated appropriate inferencedata 1110 based on expert feedback. The inference data 1110 generated byperforming the selected medical scan inference function 1105 can be sentto a client device 120 of a selected expert user, such as an expert userin the user database selected based on categorized performance dataand/or qualification data that corresponds to the scan category 1120and/or the inference itself, for example, by selecting an expert userbest suited to review an identified abnormality classifier category 444and/or abnormality pattern category 446 in the inference data 1110 basedon categorized performance data and/or qualification data of acorresponding user entry. The selected user can also correspond to amedical professional or other user employed at the originating entityand/or corresponding to the originating medical professional, indicatedin the originating entity data 423.

FIG. 6B illustrates an embodiment of the medical scan diagnosing system108 in conjunction with performing a remediation step 1140. The medicalscan diagnosing system 108 can monitor the performance of the set ofmedical scan inference functions 1105, for example, based on evaluatinginference accuracy data outputted by an inference data evaluationfunction and/or based monitoring on the performance score data 630 inthe medical scan analysis function database, and can determine whetheror not if the corresponding medical scan inference function 1105 isperforming properly. This can include, for example, determining if aremediation step 1140 is necessary for a medical scan inference function1105, for example, by comparing the performance score data 630 and/orinference accuracy data to remediation criteria data 652. Determining ifa remediation step 1140 is necessary can also be based on receiving anindication from the expert user or another user that remediation isnecessary for one or more identified medical scan inference functions1105 and/or for all of the medical scan inference functions 1105.

In various embodiments, a remediation evaluation function is utilized todetermine if a remediation step 1140 is necessary for medical scaninference function 1105. The remediation evaluation function can includedetermining that remediation is necessary when recent accuracy dataand/or efficiency data of a particular medical scan inference function1105 is below the normal performance level of the particular inferencefunction. The remediation evaluation function can include determiningthat remediation is necessary when recent or overall accuracy dataand/or efficiency data of a particular medical scan inference function1105 is below a recent or overall average for all or similar medicalscan inference functions 1105. The remediation evaluation function caninclude determining that remediation is necessary only after a thresholdnumber of incorrect diagnoses are made. In various embodiments, multiplethreshold number of incorrect diagnoses correspond to differentdiagnoses categories. For example, the threshold number of incorrectdiagnoses for remediation can be higher for false negative diagnosesthan false positive diagnoses. Similarly, categories corresponding todifferent diagnosis severities and/or rarities can have differentthresholds, for example where a threshold number of more severe and/ormore rare diagnoses that were inaccurate to necessitate remediation islower than a threshold number of less severe and/or less rare diagnosesthat were inaccurate.

The remediation step 1140 can include automatically updating anidentified medical inference function 1105. This can includeautomatically retraining identified medical inference function 1105 onthe same training set or on a new training set that includes new data,data with higher corresponding confidence scores, or data selected basedon new training set criteria. The identified medical inference function1105 can also be updated and/or changed based on the review datareceived from the client device. For example, the medical scan andexpert feedback data can be added to the training set of the medicalscan inference function 1105, and the medical scan inference function1105 can be retrained on the updated training set. Alternatively or inaddition, the expert user can identify additional parameters and/orrules in the expert feedback data based on the errors made by theinference function in generating the inference data 1110 for the medicalscan, and these parameters and/or rules can be applied to update themedical scan inference function, for example, by updating the model typedata 622 and/or model parameter data 623.

The remediation step 1140 can also include determining to split a scancategory 1120 into two or more subcategories. Thus, two or more newmedical scan inference functions 1105 can be created, where each newmedical scan inference functions 1105 is trained on a correspondingtraining set that is a subset of the original training set and/orincludes new medical scan data corresponding to the subcategory. Thiscan allow medical scan inference functions 1105 to become morespecialized and/or allow functions to utilize characteristics and/ordiscrepancies specific to the subcategory when generating inference data1110. Similarly, a new scan category 1120 that was not previouslyrepresented by any of the medical scan inference functions 1105 can beadded in the remediation step, and a new medical scan inferencefunctions 1105 can be trained on a new set of medical scan data thatcorresponds to the new scan category 1120. Splitting a scan categoryand/or adding a scan category can be determined automatically by themedical scan diagnosing system 108 when performing the remediation step1140, for example, based on performance score data 630. This can also bedetermined based on receiving instructions to split a category and/oradd a new scan category from the expert user or other user of thesystem.

After a medical scan inference function 1105 is updated or created forthe first time, the remediation step 1140 can further undergo acommissioning test, which can include rigorous testing of the medicalscan inference function 1105 on a testing set, for example, based on thetraining parameters 620. For example, the commissioning test can bepassed when the medical scan inference function 1105 generates athreshold number of correct inference data 1110 and/or the test can bepassed if an overall or average discrepancy level between the inferencedata and the test data is below a set error threshold. The commissioningtest can also evaluate efficiency, where the medical scan inferencefunction 1105 only passes the commissioning test if it performs at orexceeds a threshold efficiency level. If the medical scan inferencefunction 1105 fails the commissioning test, the model type and/or modelparameters can be modified automatically or based on user input, and themedical scan inference function can be retested, continuing this processuntil the medical scan inference function 1105 passes the commissioningtest.

The remediation step 1140 can include decommissioning the medical scaninference function 1105, for example, while the medical scan inferencefunction is being retrained and/or is undergoing the commissioning test.Incoming scans to the medical scan diagnosing system 108 with a scancategory 1120 corresponding to a decommissioned medical scan inferencefunction 1105 can be sent directly to review by one or more users, forexample, in conjunction with the medical scan annotator system 106.These user-reviewed medical scans and corresponding annotations can beincluded in an updated training set used to train the decommissionedmedical scan inference function 1105 as part of the remediation step1140. In some embodiments, previous versions of the plurality of medicalscan image analysis functions can be stored in memory of the medicalscan diagnosing system and/or can be determined based on the versiondata 640 of a medical scan inference function 1105. A previous versionof a medical scan inference function 1105, such as most recent versionor version with the highest performance score, can be utilized duringthe remediation step 1140 as an alternative to sending all medical scansto user review.

A medical scan inference function can also undergo the remediation step1140 automatically in response to a hardware and/or software update onprocessing, memory, and/or other computing devices where the medicalscan inference function 1105 is stored and/or performed. Differentmedical scan inference functions 1105 can be containerized on their owndevices by utilizing a micro-service architecture, so hardware and/orsoftware updates may only necessitate that one of the medical scaninference functions 1105 undergo the remediation step 1140 while theothers remain unaffected. A medical scan inference function 1105 canalso undergo the remediation step 1140 automatically in response tonormal system boot-up, and/or periodically in fixed intervals. Forexample, in response to a scheduled or automatically detected hardwareand/or software update, change, or issue, one or more medical scaninference functions 1105 affected by this hardware or software can betaken out of commission until they each pass the commissioning test.Such criteria can be indicated in the remediation criteria data 652.

The medical scan diagnosing system 108 can automatically manage usagedata, subscription data, and/or billing data for the plurality of userscorresponding to user usage of the system, for example, by utilizing,generating, and/or updating some or all of the subscription data of theuser database. Users can pay for subscriptions to the system, which caninclude different subscription levels that can correspond to differentcosts. For example, a hospital can pay a monthly cost to automaticallydiagnose up to 100 medical scans per month. The hospital can choose toupgrade their subscription or pay per-scan costs for automaticdiagnosing of additional scans received after the quota is reachedand/or the medical scan diagnosing system 108 can automatically sendmedical scans received after the quota is reached to an expert userassociated with the hospital. In various embodiments incentive programscan be used by the medical scan diagnosing system to encourage expertsto review medical scans from different medical entities. For example, anexpert can receive credit to their account and/or subscription upgradesfor every medical scan reviewed, or after a threshold number of medicalscans are reviewed. The incentive programs can include interactions by auser with other subsystems, for example, based on contributions made tomedical scan entries via interaction with other subsystems.

FIG. 7A presents an embodiment of a medical scan image analysis system112. A training set of medical scans used to train one more medical scanimage analysis functions can be received from one or more client devicesvia the network and/or can be retrieved from the medical scan database342, for example, based on training set data 621 corresponding tomedical scan image analysis functions. Training set criteria, forexample, identified in training parameters 620 of the medical scan imageanalysis function, can be utilized to automatically identify and selectmedical scans to be included in the training set from a plurality ofavailable medical scans. The training set criteria can be automaticallygenerated based on, for example, previously learned criteria, and/ortraining set criteria can be received via the network, for example, froman administrator of the medical scan image analysis system. The trainingset criteria can include a minimum training set size. The training setcriteria can include data integrity requirements for medical scans inthe training set such as requiring that the medical scan is assigned atruth flag 461, requiring that performance score data for a hospitaland/or medical professional associated with the medical scan comparesfavorably to a performance score threshold, requiring that the medicalscan has been reviewed by at least a threshold number of medicalprofessionals, requiring that the medical scan and/or a diagnosiscorresponding to a patient file of the medical scan is older than athreshold elapsed time period, or based on other criteria intended toinsure that the medical scans and associated data in the training set isreliable enough to be considered “truth” data. The training set criteriacan include longitudinal requirements such the number of requiredsubsequent medical scans for the patient, multiple required types ofadditional scans for the patient, and/or other patient filerequirements.

The training set criteria can include quota and/or proportionrequirements for one or more medical scan classification data. Forexample, the training set criteria can include meeting quota and/orproportion requirements for one or more scan types and/or human bodylocation of scans, meeting quota or proportion requirements for a numberof normal medical scans and a number of medicals scans with identifiedabnormalities, meeting quota and/or proportion requirements for a numberof medical scans with abnormalities in certain locations and/or a numberof medical scans with abnormalities that meet certain size, type, orother characteristics, meeting quota and/or proportion data for a numberof medical scans with certain diagnosis or certain corresponding medicalcodes, and/or meeting other identified quota and/or proportion datarelating to metadata, patient data, or other data associated with themedical scans.

In some embodiments, multiple training sets are created to generatecorresponding medical scan image analysis functions, for example,corresponding to some or all of the set of medical scan inferencefunctions 1105. Some or all training sets can be categorized based onsome or all of the scan classifier data 420 as described in conjunctionwith the medical scan diagnosing system 108, where medical scans areincluded in a training set based on their scan classifier data 420matching the scan category of the training set. In some embodiments, theinput quality assurance function 1106 or another input check step can beperformed on medical scans selected for each training set to confirmthat their corresponding scan classifier data 420 is correct. In someembodiments, the input quality assurance function can correspond to itsown medical scan image analysis function, trained by the medical scanimage analysis system, where the input quality assurance functionutilizes high level computer vision technology to determine a scancategory 1120 and/or to confirm the scan classifier data 420 alreadyassigned to the medical scan.

In some embodiments, the training set will be used to create a singleneural network model, or other model corresponding to model type data622 and/or model parameter data 623 of the medical scan image analysisfunction that can be trained on some or all of the medical scanclassification data described above and/or other metadata, patient data,or other data associated with the medical scans. In other embodiments, aplurality of training sets will be created to generate a plurality ofcorresponding neural network models, where the multiple training setsare divided based on some or all of the medical scan classification datadescribed above and/or other metadata, patient data, or other dataassociated with the medical scans. Each of the plurality of neuralnetwork models can be generated based on the same or different learningalgorithm that utilizes the same or different features of the medicalscans in the corresponding one of the plurality of training sets. Themedical scan classifications selected to segregate the medical scansinto multiple training sets can be received via the network, for examplebased on input to an administrator client device from an administrator.The medical scan classifications selected to segregate the medical scanscan be automatically determined by the medical scan image analysissystem, for example, where an unsupervised clustering algorithm isapplied to the original training set to determine appropriate medicalscan classifications based on the output of the unsupervised clusteringalgorithm.

In embodiments where the medical scan image analysis system is used inconjunction with the medical scan diagnosing system, each of the medicalscan image analysis functions associated with each neural network modelcan correspond to one of the plurality of neural network modelsgenerated by the medical scan image analysis system. For example, eachof the plurality of neural network models can be trained on a trainingset classified on scan type, scan human body location, hospital or otheroriginating entity data, machine model data, machine calibration data,contrast agent data, geographic region data, and/or other scanclassifying data as discussed in conjunction with the medical scandiagnosing system. In embodiments where the training set classifiers arelearned, the medical scan diagnosing system can determine which of themedical scan image analysis functions should be applied based on thelearned classifying criteria used to segregate the original trainingset.

A computer vision-based learning algorithm used to create each neuralnetwork model can include selecting a three-dimensional subregion 1310for each medical scan in the training set. This three-dimensionalsubregion 1310 can correspond to a region that is “sampled” from theentire scan that may represent a small fraction of the entire scan.Recall that a medical scan can include a plurality of orderedcross-sectional image slices. Selecting a three-dimensional subregion1310 can be accomplished by selecting a proper image slice subset 1320of the plurality of cross-sectional image slices from each of theplurality of medical scans, and by further selecting a two-dimensionalsubregion 1330 from each of the selected subset of cross-sectional imageslices of the each of the medical scans. In some embodiments, theselected image slices can include one or more non-consecutive imageslices and thus a plurality of disconnected three-dimensional subregionswill be created. In other embodiments, the selected proper subset of theplurality of image slices correspond to a set of consecutive imageslices, as to ensure that a single, connected three-dimensionalsubregion is selected. In some embodiments, entire scans of the trainingset are used to train the neural network model. In such embodiment, asused herein, the three-dimensional subregion 1310 can refer to all ofthe medical scan image data 410 of a medical scan.

In some embodiments, a density windowing step can be applied to the fullscan or the selected three-dimensional subregion. The density windowingstep can include utilizing a selected upper density value cut off and/ora selected lower density value cut off, and masking pixels with highervalues than the upper density value cut off and/or masking pixels withlower values than the lower density value cut off. The upper densityvalue cut off and/or a selected lower density value cut off can bedetermined based on based on the range and/or distribution of densityvalues included in the region that includes the abnormality, and/orbased on the range and/or distribution of density values associated withthe abnormality itself, based on user input to a subsystem, based ondisplay parameter data associated with the medical scan or associatedwith medical scans of the same type, and/or can be learned in thetraining step. In some embodiments, a non-linear density windowingfunction can be applied to alter the pixel density values, for example,to stretch or compress contrast. In some embodiments, this densitywindowing step can be performed as a data augmenting step, to createadditional training data for a medical scan in accordance with differentdensity windows.

Having determined the subregion training set 1315 of three-dimensionalsubregions 1310 corresponding to the set of full medical scans in thetraining set, the medical scan image analysis system can complete atraining step 1352 by performing a learning algorithm on the pluralityof three-dimensional subregions to generate model parameter data 1355 ofa corresponding learning model. The learning model can include one ormore of a neural network, an artificial neural network, a convolutionalneural network, a Bayesian model, a support vector machine model, acluster analysis model, or other supervised or unsupervised learningmodel. The model parameter data 1355 can generated by performing thelearning algorithm 1350, and the model parameter data 1355 can beutilized to determine the corresponding medical scan image analysisfunctions. For example, some or all of the model parameter data 1355 canbe mapped to the medical scan analysis function in the model parameterdata 623 or can otherwise define the medical scan analysis function.

The training step 1352 can include creating feature vectors for eachthree-dimensional subregion of the training set for use by the learningalgorithm 1350 to generate the model parameter data 1355. The featurevectors can include the pixel data of the three-dimensional subregionssuch as density values and/or grayscale values of each pixel based on adetermined density window. The feature vectors can also include otherfeatures as additional input features or desired output features, suchas known abnormality data such as location and/or classification data,patient history data such as risk factor data or previous medical scans,diagnosis data, responsible medical entity data, scan machinery model orcalibration data, contrast agent data, medical code data, annotationdata that can include raw or processed natural language text data, scantype and/or anatomical region data, or other data associated with theimage, such as some or all data of a medical scan entry 352. Featurescan be selected based on administrator instructions received via thenetwork and/or can be determined based on determining a feature set thatreduces error in classifying error, for example, by performing across-validation step on multiple models created using different featuresets. The feature vector can be split into an input feature vector andoutput feature vector. The input feature vector can include data thatwill be available in subsequent medical scan input, which can includefor example, the three-dimensional subregion pixel data and/or patienthistory data. The output feature vector can include data that will beinferred in subsequent medical scan input and can include single outputvalue, such as a binary value indicating whether or not the medical scanincludes an abnormality or a value corresponding to one of a pluralityof medical codes corresponding to the image. The output feature vectorcan also include multiple values which can include abnormality locationand/or classification data, diagnosis data, or other output. The outputfeature vector can also include a determined upper density value cut offand/or lower density value cut off, for example, characterizing whichpixel values were relevant to detecting and/or classifying anabnormality. Features included in the output feature vector can beselected to include features that are known in the training set, but maynot be known in subsequent medical scans such as triaged scans to bediagnosed by the medical scan diagnosing system, and/or scans to belabeled by the medical scan report labeling system. The set of featuresin the input feature vector and output feature vector, as well as theimportance of different features where each feature is assigned acorresponding weight, can also be designated in the model parameter data1355.

Consider a medical scan image analysis function that utilizes a neuralnetwork. The neural network can include a plurality of layers, whereeach layer includes a plurality of neural nodes. Each node in one layercan have a connection to some or all nodes in the next layer, where eachconnection is defined by a weight value. Thus, the model parameter data1355 can include a weight vector that includes weight values for everyconnection in the network. Alternatively or in addition, the modelparameter data 1355 can include any vector or set of parametersassociated with the neural network model, which can include an upperdensity value cut off and/or lower density value cut off used to masksome of the pixel data of an incoming image, kernel values, filterparameters, bias parameters, and/or parameters characterizing one ormore of a plurality of convolution functions of the neural networkmodel. The medical scan image analysis function can be utilized toproduce the output vector as a function of the input feature vector andthe model parameter data 1355 that characterizes the neural networkmodel. In particular, the medical scan image analysis function caninclude performing a forward propagation step plurality of neuralnetwork layers to produce an inferred output vector based on the weightvector or other model parameter data 1355. Thus, the learning algorithm1350 utilized in conjunction with a neural network model can includedetermining the model parameter data 1355 corresponding to the neuralnetwork model, for example, by populating the weight vector with optimalweights that best reduce output error.

In particular, determining the model parameter data 1355 can includeutilizing a backpropagation strategy. The forward propagation algorithmcan be performed on at least one input feature vector corresponding toat least one medical scan in the training set to propagate the at leastone input feature vector through the plurality of neural network layersbased on initial and/or default model parameter data 1355, such as aninitial weight vector of initial weight values set by an administratoror chosen at random. The at least one output vector generated byperforming the forward propagation algorithm on the at least one inputfeature vector can be compared to the corresponding at least one knownoutput feature vector to determine an output error. Determining theoutput error can include, for example, computing a vector distance suchas the Euclidian distance, or squared Euclidian distance, between theproduced output vector and the known output vector, and/or determiningan average output error such as an average Euclidian distance or squaredEuclidian distance if multiple input feature vectors were employed.Next, gradient descent can be performed to determine an updated weightvector based on the output error or average output error. This gradientdescent step can include computing partial derivatives for the errorwith respect to each weight, or other parameter in the model parameterdata 1355, at each layer starting with the output layer. Chain rule canbe utilized to iteratively compute the gradient with respect to eachweight or parameter at each previous layer until all weight's gradientsare computed. Next updated weights, or other parameters in the modelparameter data 1355, are generated by updating each weight based on itscorresponding calculated gradient. This process can be repeated on atleast one input feature vector, which can include the same or differentat least one feature vector used in the previous iteration, based on theupdated weight vector and/or other updated parameters in the modelparameter data 1355 to create a new updated weight vector and/or othernew updated parameters in the model parameter data 1355. This processcan continue to repeat until the output error converges, the outputerror is within a certain error threshold, or another criterion isreached to determine the most recently updated weight vector and/orother model parameter data 1355 is optimal or otherwise determined forselection.

Having determined the medical scan neural network and its final othermodel parameter data 1355, an inference step 1354 can be performed onnew medical scans to produce inference data 1370, such as inferredoutput vectors, as shown in FIG. 7B. The inference step can includeperforming the forward propagation algorithm to propagate an inputfeature vector through a plurality of neural network layers based on thefinal model parameter data 1355, such as the weight values of the finalweight vector, to produce the inference data. This inference step 1354can correspond to performing the medical scan image analysis function,as defined by the final model parameter data 1355, on new medical scansto generate the inference data 1370, for example, in conjunction withthe medical scan diagnosing system 108 to generate inferred diagnosisdata or other selected output data for triaged medical scans based onits corresponding the input feature vector.

The inference step 1354 can include applying the density windowing stepto new medical scans. Density window cut off values and/or a non-lineardensity windowing function that are learned can be automatically appliedwhen performing the inference step. For example, if the training step1352 was used to determine optimal upper density value cut off and/orlower density value cut off values to designate an optimal densitywindow, the inference step 1354 can include masking pixels of incomingscans that fall outside of this determined density window beforeapplying the forward propagation algorithm. As another example, iflearned parameters of one or more convolutional functions correspond tothe optimal upper density value cut off and/or lower density value cutoff values, the density windowing step is inherently applied when theforward propagation algorithm is performed on the new medical scans.

In some embodiments where a medical scan analysis function is defined bymodel parameter data 1355 corresponding to a neutral network model, theneural network model can be a fully convolutional neural network. Insuch embodiments, only convolution functions are performed to propagatethe input feature vector through the layers of the neural network in theforward propagation algorithm. This enables the medical scan imageanalysis functions to process input feature vectors of any size. Forexample, as discussed herein, the pixel data corresponding to thethree-dimensional subregions is utilized input to the forwardpropagation algorithm when the training step 1352 is employed topopulate the weight vector and/or other model parameter data 1355.However, when performing the forward propagation algorithm in theinference step 1354, the pixel data of full medical scans can beutilized as input, allowing the entire scan to be processed to detectand/or classify abnormalities, or otherwise generate the inference data1370. This may be a preferred embodiment over other embodiments wherenew scans must also be sampled by selecting a three-dimensionalsubregions and/or other embodiments where the inference step requires“piecing together” inference data 1370 corresponding to multiplethree-dimensional subregions processed separately.

The inferred output vector of the inference data 1370 can include aplurality of abnormality probabilities mapped to a pixel location ofeach of a plurality of cross-sectional image slices of the new medicalscan. For example, the inferred output vector can indicate a set ofprobability matrices 1371, where each matrix in the set corresponds toone of the plurality of image slices of the medical scan, where eachmatrix is a size corresponding to the number of pixels in each imageslice, where each cell of each matrix corresponds to a pixel of thecorresponding image slice, whose value is the abnormality probability ofthe corresponding pixel.

A detection step 1372 can include determining if an abnormality ispresent in the medical scan based on the plurality of abnormalityprobabilities. Determining if an abnormality is present can include, forexample, determining that a cluster of pixels in the same region of themedical scan correspond to high abnormality probabilities, for example,where a threshold proportion of abnormality probabilities must meet orexceed a threshold abnormality probability, where an average abnormalityprobability of pixels in the region must meet or exceed a thresholdabnormality probability, where the region that includes the cluster ofpixels must be at least a certain size, etc. Determining if anabnormality is present can also include calculating a confidence scorebased on the abnormality probabilities and/or other data correspondingto the medical scan such as patient history data. The location of thedetected abnormality can be determined in the detection step 1372 basedon the location of the pixels with the high abnormality probabilities.The detection step can further include determining an abnormality region1373, such as a two-dimensional subregion on one or more image slicesthat includes some or all of the abnormality. The abnormality region1373 determined in the detection step 1372 can be mapped to the medicalscan to populate some or all of the abnormality location data 443 foruse by one or more other subsystems 101 and/or client devices 120.Furthermore, determining whether or not an abnormality exists in thedetection step 1372 can be used to populate some or all of the diagnosisdata 440 of the medical scan, for example, to indicate that the scan isnormal or contains an abnormality in the diagnosis data 440.

An abnormality classification step 1374 can be performed on a medicalscan in response to determining an abnormality is present.Classification data 1375 corresponding to one or more classificationcategories such as abnormality size, volume, pre-post contract, doublingtime, calcification, components, smoothness, texture, diagnosis data,one or more medical codes, a malignancy rating such as a Lung-RADSscore, or other classifying data as described herein can be determinedbased on the detected abnormality. The classification data 1375generated by the abnormality classification step 1374 can be mapped tothe medical scan to populate some or all of the abnormalityclassification data 445 of the corresponding abnormality classifiercategories 444 and/or abnormality pattern categories 446 and/or todetermine one or more medical codes 447 of the medical scan. Theabnormality classification step 1374 can include performing anabnormality classification function on the full medical scan, or theabnormality region 1373 determined in the detection step 1372. Theabnormality classification function can be based on another modeltrained on abnormality data such as a support vector machine model,another neural network model, or any supervised classification modeltrained on medical scans, or portions of medical scans, that includeknown abnormality classifying data to generate inference data for someor all of the classification categories. For example, the abnormalityclassification function can include another medical scan analysisfunction. Classification data 1375 in each of a plurality ofclassification categories can also be assigned their own calculatedconfidence score, which can also be generated by utilizing theabnormality classification function. Output to the abnormalityclassification function can also include at least one identified similarmedical scan and/or at least one identified similar cropped image, forexample, based on the training data. The abnormality classification stepcan also be included in the inference step 1354, where the inferredoutput vector or other inference data 1370 of the medical scan imageanalysis function includes the classification data 1375.

The abnormality classification function can be trained on full medicalscans and/or one or more cropped or full selected image slices frommedical scans that contain an abnormality. For example, the abnormalityclassification function can be trained on a set of two-dimensionalcropped slices that include abnormalities. The selected image slicesand/or the cropped region in each selected image slice for each scan inthe training set can be automatically selected based upon the knownlocation of the abnormality. Input to the abnormality classificationfunction can include the full medical scan, one or more selected fullimage slices, and/or one or more selected image slices cropped based ona selected region. Thus, the abnormality classification step can includeautomatically selecting one or more image slices that include thedetected abnormality. The slice selection can include selecting thecenter slice in a set of consecutive slices that are determined toinclude the abnormality or selecting a slice that has the largestcross-section of the abnormality, or selecting one or more slices basedon other criteria. The abnormality classification step can also includeautomatically generating one or more cropped two-dimensional imagescorresponding to the one or more of the selected image slices based onan automatically selected region that includes the abnormality.

Input to the abnormality classification function can also include otherdata associated with the medical scan, including patient history, riskfactors, or other metadata. The abnormality classification step can alsoinclude determining some or all of the characteristics based on data ofthe medical scan itself. For example, the abnormality size and volumecan be determined based on a number of pixels determined to be part ofthe detected abnormality. Other classifiers such as abnormality textureand/or smoothness can be determined by performing one or more otherpreprocessing functions on the image specifically designed tocharacterize such features. Such preprocessed characteristics can beincluded in the input to the abnormality classification function to themore difficult task of assigning a medical code or generating otherdiagnosis data. The training data can also be preprocessed to includesuch preprocessed features.

A similar scan identification step 1376 can also be performed on amedical scan with a detected abnormality and/or can be performed on theabnormality region 1373 determined in the detection step 1372. Thesimilar scan identification step 1376 can include generating similarabnormality data 1377, for example, by identifying one or more similarmedical scans or one or more similar cropped two-dimensional images froma database of medical scans and/or database of cropped two-dimensionalimages. Similar medical scans and/or cropped images can include medicalscans or cropped images that are visually similar, medical scans orcropped images that have known abnormalities in a similar location to aninferred abnormality location of the given medical scan, medical scansthat have known abnormalities with similar characteristics to inferredcharacteristics of an abnormality in the given scan, medical scans withsimilar patient history and/or similar risk factors, or some combinationof these factors and/or other known and/or inferred factors. The similarabnormality data 1377 can be mapped to the medical scan to populate someor all of its corresponding similar scan data 480 for use by one or moreother subsystems 101 and/or client devices 120.

The similar scans identification step 1376 can include performing a scansimilarity algorithm, which can include generating a feature vector forthe given medical scan and for medical scans in the set of medicalscans, where the feature vector can be generated based on quantitativeand/or category based visual features, inferred features, abnormalitylocation and/or characteristics such as the predetermined size and/orvolume, patient history and/or risk factor features, or other known orinferred features. A medical scan similarity analysis function can beapplied to the feature vector of the given medical scan and one or morefeature vectors of medical scans in the set. The medical scan similarityanalysis function can include computing a similarity distance such asthe Euclidian distance between the feature vectors, and assigning thesimilarity distance to the corresponding medical scan in the set.Similar medical scans can be identified based on determining one or moremedical scans in the set with a smallest computed similarity distance,based on ranking medical scans in the set based on the computedsimilarity distances and identifying a designated number of top rankedmedical scans, and/or based on determining if a similarity distancebetween the given medical scan and a medical scan in the set is smallerthan a similarity threshold. Similar medical scans can also beidentified based on determining medical scans in a database that mappedto a medical code that matches the medical code of the medical scan, ormapped to other matching classifying data. A set of identified similarmedical scans can also be filtered based on other inputted orautomatically generated criteria, where for example only medical scanswith reliable diagnosis data or rich patient reports, medical scans withcorresponding with longitudinal data in the patient file such asmultiple subsequent scans taken at later dates, medical scans withpatient data that corresponds to risk factors of the given patient, orother identified criteria, where only a subset of scans that comparefavorably to the criteria are selected from the set and/or only ahighest ranked single scan or subset of scans are selected from the set,where the ranking is automatically computed based on the criteria.Filtering the similar scans in this fashion can include calculating, orcan be based on previously calculated, one or more scores as discussedherein. For example, the ranking can be based on a longitudinal qualityscore, such as the longitudinal quality score 434, which can becalculated for an identified medical scan based on a number ofsubsequent and/or previous scans for the patient. Alternatively or inaddition, the ranking can be based on a confidence score associated withdiagnosis data of the scan, such as confidence score data 460, based onperformance score data associated with a user or medical entityassociated with the scan, based on an amount of patient history data ordata in the medical scan entry 352, or other quality factors. Theidentified similar medical scans can be filtered based on ranking thescans based on their quality score and/or based on comparing theirquality score to a quality score threshold. In some embodiments, alongitudinal threshold must be reached, and only scans that comparefavorably to the longitudinal threshold will be selected. For example,only scans with at least three scans on file for the patient and finalbiopsy data will be included.

In some embodiments, the similarity algorithm can be utilized inaddition to or instead of the trained abnormality classificationfunction to determine some or all of the inferred classification data1375 of the medical scan, based on the classification data such asabnormality classification data 445 or other diagnosis data 440 mappedto one or more of the identified similar scans. In other embodiments,the similarity algorithm is merely used to identify similar scans forreview by medical professionals to aid in review, diagnosis, and/orgenerating medical reports for the medical image.

A display parameter step 1378 can be performed based on the detectionand/or classification of the abnormality. The display parameter step caninclude generating display parameter data 1379, which can includeparameters that can be used by an interactive interface to best displayeach abnormality. The same or different display parameters can begenerated for each abnormality. The display parameter data generated inthe display parameter step 1378 can be mapped to the medical scan topopulate some or all of its corresponding display parameter data 470 foruse by one or more other subsystems 101 and/or client devices 120.

Performing the display parameter step 1378 can include selecting one ormore image slices that include the abnormality by determining the one ormore image slices that include the abnormality and/or determining one ormore image slices that has a most optimal two-dimensional view of theabnormality, for example by selecting the center slice in a set ofconsecutive slices that are determined to include the abnormality,selecting a slice that has the largest cross-section of the abnormality,selecting a slice that includes a two-dimensional image of theabnormality that is most similar to a selected most similartwo-dimensional-image, selecting the slice that was used as input to theabnormality classification step and/or similar scan identification step,or based on other criteria. This can also include automatically croppingone or more selected image slices based on an identified region thatincludes the abnormality. This can also select an ideal Hounsfieldwindow that best displays the abnormality. This can also includeselecting other display parameters based on data generated by themedical scan interface evaluating system and based on the medical scan.

FIGS. 8A-8F illustrate embodiments of a medical picture archiveintegration system 2600. The medical picture archive integration system2600 can provide integration support for a medical picture archivesystem 2620, such as a PACS that stores medical scans. The medicalpicture archive integration system 2600 can utilize model parametersreceived from a central server system 2640 via a network 2630 to performan inference function on de-identified medical scans of medical scansreceived from the medical picture archive system 2620. The annotationdata produced by performing the inference function can be transmittedback to the medical picture archive system. Furthermore, the annotationdata and/or de-identified medical scans can be sent to the centralserver system 2640, and the central server system can train on thisinformation to produce new and/or updated model parameters fortransmission back to the medical picture archive integration system 2600for use on subsequently received medical scans.

In various embodiments, medical picture archive integration system 2600includes a de-identification system that includes a first memorydesignated for protected health information (PHI), operable to perform ade-identification function on a DICOM image, received from a medicalpicture archive system, to identify at least one patient identifier andgenerate a de-identified medical scan that does not include the at leastone patient identifier. The medical picture archive integration systemfurther includes a de-identified image storage system that stores thede-identified medical scan in a second memory that is separate from thefirst memory, and an annotating system, operable to utilize modelparameters received from a central server to perform an inferencefunction on the de-identified medical scan, retrieved from the secondmemory to generate annotation data for transmission to the medicalpicture archive system as an annotated DICOM file.

The first memory and the second memory can be implemented by utilizingseparate storage systems: the first memory can be implemented by a firststorage system designated for PHI storage, and the second memory can beimplemented by a second storage system designated for storage ofde-identified data. The first storage system can be protected fromaccess by the annotating system, while the second storage system can beaccessible by the annotating system. The medical picture archiveintegration system 2600 can be operable to perform the de-identificationfunction on data in first storage system to generate de-identified data.The de-identified data can then be stored in the second storage systemfor access by the annotating system. The first and second storagesystems can be physically separate, each utilizing at least one of theirown, separate memory devices. Alternatively, the first and secondstorage systems can be virtually separate, where data is stored inseparate virtual memory locations on the same set of memory devices.Firewalls, virtual machines, and/or other protected containerization canbe utilized to enforce the separation of data in each storage system, toprotect the first storage system from access by the annotating systemand/or from other unauthorized access, and/or to ensure that only dataof the first storage system that has been properly de-identified throughapplication of the de-identification function can be stored in thesecond storage system.

As shown in FIG. 8A, the medical picture archive system 2620 can receiveimage data from a plurality of modality machines 2622, such as CTmachines, Mill machines, x-ray machines, and/or other medical imagingmachines that produce medical scans. The medical picture archive system2620 can store this image data in a DICOM image format and/or can storethe image data in a plurality of medical scan entries 352 as describedin conjunction with some or all of the attributes described inconjunction with FIGS. 4A and 4B. While “DICOM image” will be usedherein to refer to medical scans stored by the medical picture archivesystem 2620, the medical picture archive integration system 2600 canprovide integration support for medical picture archive systems 2620that store medical scans in other formats.

The medical picture archive integration system 2600 can include areceiver 2602 and a transmitter 2604, operable to transmit and receivedata from the medical picture archive system 2620, respectively. Forexample, the receiver 2602 and transmitter 2604 can be configured toreceive and transmit data, respectively, in accordance with a DICOMcommunication protocol and/or another communication protocol recognizedby the medical image archive system 2620. The receiver can receive DICOMimages from the medical picture archive system 2620. The transmitter2604 can send annotated DICOM files to the medical picture archivesystem 2620.

DICOM images received via receiver 2602 can be sent directly to ade-identification system 2608. The de-identification system 2608 can beoperable to perform a de-identification function on the first DICOMimage to identify at least one patient identifier in the DICOM image,and to generate a de-identified medical scan that does not include theidentified at least one patient identifier. As used herein, a patientidentifier can include any patient identifying data in the image data,header, and/or metadata of a medical scan, such as a patient ID numberor other unique patient identifier, an accession number, aservice-object pair (SOP) instance unique identifier (UID) field, scandate and/or time that can be used to determine the identity of thepatient that was scanned at that date and/or time, and/or other privatedata corresponding to the patient, doctor, or hospital. In someembodiments, the de-identified medical scan is still in a DICOM imageformat. For example, a duplicate DICOM image that does not include thepatient identifiers can be generated, and/or the original DICOM imagecan be altered such that the patient identifiers of the new DICOM imageare masked, obfuscated, removed, replaced with a custom fiducial, and/orotherwise anonymized. In other embodiments, the de-identified medicalscan is formatted in accordance with a different image format and/ordifferent data format that does not include the identifying information.In some embodiments, other private information, for example, associatedwith a particular doctor or other medical professional, can beidentified and anonymized as well.

Some patient identifying information can be included in a DICOM headerof the DICOM image, for example, in designated fields for patientidentifiers. These corresponding fields can be anonymized within thecorresponding DICOM header field. Other patient identifying informationcan be included in the image itself, such as in medical scan image data410. For example, the image data can include a patient name or otheridentifier that was handwritten on a hard copy of the image before theimage was digitized. As another example, a hospital administered armbandor other visual patient information in the vicinity of the patient mayhave been captured in the image itself. A computer vision model candetect the presence of these identifiers for anonymization, for example,where a new DICOM image includes a fiducial image that covers theidentifying portion of the original DICOM image. In some embodiments,patient information identified in the DICOM header can be utilized todetect corresponding patient information in the image itself. Forexample, a patient name extracted from the DICOM header beforeanonymization can be used to search for the patient name in the imageand/or to detect a location of the image that includes the patient name.In some embodiments, the de-identification system 2608 is implemented bythe de-identification system discussed in conjunction with FIGS. 10A,10B and 11, and/or utilizes functions and/or operations discussed inconjunction with FIGS. 10A, 10B and 11.

The de-identified medical scan can be stored in de-identified imagestorage system 2610 and the annotating system 2612 can access thede-identified medical scan from the de-identified image storage system2610 for processing. The de-identified storage system can archive aplurality of de-identified DICOM images and/or can serve as temporarystorage for the de-identified medical scan until processing of thede-identified medical scan by the annotating system 2612 is complete.The annotating system 2612 can generate annotation data by performing aninference function on the de-identified medical scan, utilizing themodel parameters received from the central server system 2640. Theannotation data can correspond to some or all of the diagnosis data 440as discussed in conjunction with FIGS. 4A and 4B. In come embodiments,the annotating system 2612 can utilize the model parameters to performinference step 1354, the detection step 1372, the abnormalityclassification step 1374, the similar scan identification step 1376,and/or the display parameter step 1378 of the medical scan imageanalysis system 112, as discussed in conjunction with FIG. 7B, onde-identified medical scans received from the medical picture archivesystem 2620.

In some embodiments, model parameters for a plurality of inferencefunctions can be received from the central server system 2640, forexample, where each inference function corresponds to one of a set ofdifferent scan categories. Each scan category can correspond to a uniquecombination of one or a plurality of scan modalities, one of a pluralityof anatomical regions, and/or other scan classifier data 420. Forexample, a first inference function can be trained on and intended forde-identified medical scans corresponding chest CT scans, and a secondinference function can be trained on and intended for de-identifiedmedical scans corresponding to head MRI scans. The annotating system canselect one of the set of inference functions based on determining thescan category of the DICOM image, indicated in the de-identified medicalscan, and selecting the inference function that corresponds to thedetermined scan category.

To ensure that scans received from the medical picture archive system2620 match the set of scan categories for which the annotating system isoperable to perform a corresponding inference function, the transmittercan transmit requests, such as DICOM queries, indicating image typeparameters such as parameters corresponding to scan classifier data 420,for example indicating one or more scan modalities, one or moreanatomical regions, and/or other parameters. For example, the requestcan indicate that all incoming scans that match the set of scancategories corresponding to a set of inference functions the annotatingsystem 2612 for which the annotating system has obtained modelparameters from the central server system 2640 and is operable toperform.

Once the annotation data is generated by performing the selectedinference function, the annotating system 2612 can generate an annotatedDICOM file for transmission to the medical image archive system 2620 forstorage. The annotated DICOM file can include some or all of the fieldsof the diagnosis data 440 and/or abnormality annotation data 442 ofFIGS. 4A and 4B. The annotated DICOM file can include scan overlay data,providing location data of an identified abnormality and/or display datathat can be used in conjunction with the original DICOM image toindicate the abnormality visually in the DICOM image and/or to otherwisevisually present the annotation data, for example, for use with themedical scan assisted review system 102. For example, a DICOMpresentation state file can be generated to indicate the location of anabnormality identified in the de-identified medical scan. The DICOMpresentation state file can include an identifier of the original DICOMimage, for example, in metadata of the DICOM presentation state file, tolink the annotation data to the original DICOM image. In otherembodiments, a full, duplicate DICOM image is generated that includesthe annotation data with an identifier linking this duplicate annotatedDICOM image to the original DICOM image.

The identifier linking the annotated DICOM file to the original DICOMimage can be extracted from the original DICOM file by thede-identification system 2608, thus enabling the medical picture archivesystem 2620 to link the annotated DICOM file to the original DICOM imagein its storage. For example, the de-identified medical scan can includean identifier that links the de-identified medical scan to the originalDICOM file, but does not link the de-identified medical scan to apatient identifier or other private data.

In some embodiments, generating the annotated DICOM file includesaltering one or more fields of the original DICOM header. For example,standardized header formatting function parameters can be received fromthe central server system and can be utilized by the annotating systemto alter the original DICOM header to match a standardized DICOM headerformat. The standardized header formatting function can be trained in asimilar fashion to other medical scan analysis functions discussedherein and/or can be characterized by some or all fields of a medicalscan analysis function entry 356. The annotating system can perform thestandardized header formatting function on a de-identified medical scanto generate a new, standardized DICOM header for the medical scan to besent back to the medical picture archive system 2620 in the annotatedDICOM file and/or to replace the header of the original DICOM file. Thestandardized header formatting function can be run in addition to otherinference functions utilized to generate annotation data. In otherembodiments, the medical picture archive integration system 2600 isimplemented primarily for header standardization for medical scansstored by the medical picture archive system 2620. In such embodiments,only the standardized header formatting function is performed on thede-identified data to generate a modified DICOM header for the originalDICOM image, but the de-identified medical scan is not annotated.

In some embodiments of header standardization, the annotation system canstore a set of acceptable, standardized entries for some or all of theDICOM header fields, and can select one of the set of acceptable,standardized entries in populating one or more fields of the new DICOMheader for the annotated DICOM file. For example, each of the set ofscan categories determined by the annotating system can correspond to astandardized entry of one or more fields of the DICOM header. The newDICOM header can thus be populated based on the determined scancategory.

In some embodiments, each of the set of standardized entries can bemapped to a set of related, non-standardized entries, such as entries ina different order, commonly misspelled entries, or other similar entriesthat do not follow a standardized format. For example, one of the set ofacceptable, standardized entries for a field corresponding to a scancategory can include “Chest CT”, which can be mapped to a set ofsimilar, non-standardized entries which can include “CT chest”,“computerized topography CT”, and/or other entries that are notstandardized. In such embodiments, the annotating system can determinethe original DICOM header is one of the similar non-standardizedentries, and can select the mapped, standardized entry as the entry forthe modified DICOM header. In other embodiments, the image data itselfand/or or other header data can be utilized by the annotation system todetermine a standardized field. For example, an input quality assurancefunction 1106 can be trained by the central server system and sent tothe annotating system to determine one or more appropriate scanclassifier fields, or one or more other DICOM header fields, based onthe image data or other data of the de-identified medical scan. One ormore standardized labels can be assigned to corresponding fields of themodified DICOM header based on the one or more fields determined by theinput quality assurance function.

In some embodiments, the DICOM header is modified based on theannotation data generated in performing the inference function. Inparticular, a DICOM priority header field can be generated and/ormodified automatically based on the severity and/or time-sensitivity ofthe abnormalities detected in performing the inference function. Forexample, a DICOM priority header field can be changed from a lowpriority to a high priority in response to annotation data indicating abrain bleed in the de-identified medical scan of a DICOM imagecorresponding to a head CT scan, and a new DICOM header that includesthe high priority DICOM priority header field can be sent back to themedical picture archive system 2620 to replace or otherwise be mapped tothe original DICOM image of the head CT scan.

In various embodiments, the medical picture archive system 2620 isdisconnected from network 2630, for example, to comply with requirementsregarding Protected Health Information (PHI), such as patientidentifiers and other private patient information included in the DICOMimages and/or otherwise stored by the medical picture archive system2620. The medical picture archive integration system 2600 can enableprocessing of DICOM images while still protecting private patientinformation by first de-identifying DICOM data by utilizingde-identification system 2608. The de-identification system 2608 canutilize designated processors and memory of the medical picture archiveintegration system, for example, designated for PHI. Thede-identification system 2608 can be decoupled from the network 2630 toprevent the DICOM images that still include patient identifiers frombeing accessed via the network 2630. For example, as shown in FIG. 8A,the de-identification system 2608 is not connected to network interface2606. Furthermore, only the de-identification system 2608 has access tothe original DICOM files received from the medical picture archivesystem 2620 via receiver 2602. The de-identified image storage system2610 and annotating system 2612, as they are connected to network 2630via network interface 2606, only store and have access to thede-identified medical scan produced by the de-identification system2608.

This containerization that separates the de-identification system 2608from the de-identified image storage system 2610 and the annotatingsystem 2612 is further illustrated in FIG. 8B, which presents anembodiment of the medical picture archive integration system 2600. Thede-identification system 2608 can include its own designated memory 2654and processing system 2652, connected to receiver 2602 via bus 2659. Forexample, this memory 2654 and processing system 2652 can be designatedfor PHI, and can adhere to requirements for handling PHI. The memory2654 can store executable instructions that, when executed by theprocessing system 2652, enable the de-identification system to performthe de-identification function on DICOM images received via receiver2602 of the de-identification system. The incoming DICOM images can betemporarily stored in memory 2654 for processing, and patientidentifiers detected in performing the de-identification function can betemporarily stored in memory 2654 to undergo anonymization. Interface2655 can transmit the de-identified medical scan to interface 2661 foruse by the de-identified image storage system 2610 and the annotatingsystem 2612. Interface 2655 can be protected from transmitting originalDICOM files and can be designated for transmission of de-identifiedmedical scan only.

Bus 2669 connects interface 2661, as well as transmitter 2604 andnetwork interface 2606, to the de-identified image storage system 2610and the annotating system 2612. The de-identified image storage system2610 and annotating system 2612 can utilize separate processors andmemory, or can utilize shared processors and/or memory. For example, thede-identified image storage system 2610 can serve as temporary memory ofthe annotating system 2612 as de-identified images are received andprocessed to generate annotation data.

As depicted in FIG. 8B, the de-identified image storage system 2610 caninclude memory 2674 that can temporarily store incoming de-identifiedmedical scans as it undergoes processing by the annotating system 2612and/or can archive a plurality of de-identified medical scanscorresponding to a plurality of DICOM images received by the medicalpicture archive integration system 2600. The annotating system 2612 caninclude a memory 2684 that stores executable instructions that, whenexecuted by processing system 2682, cause the annotating system 2612perform a first inference function on de-identified medical scan togenerate annotation data by utilizing the model parameters received viainterface 2606, and to generate an annotated DICOM file based on theannotation data for transmission via transmitter 2604. The modelparameters can be stored in memory 2684, and can include modelparameters for a plurality of inference functions, for example,corresponding to a set of different scan categories.

The medical picture archive integration system can be an onsite system,installed at a first geographic site, such as a hospital or othermedical entity that is affiliated with the medical picture archivesystem 2620. The hospital or other medical entity can further beresponsible for the PHI of the de-identification system, for example,where the memory 2654 and processing system 2652 are owned by,maintained by, and/or otherwise affiliated with the hospital or othermedical entity. The central server system 2640 can be located at asecond, separate geographic site that is not affiliated with thehospital or other medical entity and/or at a separate geographic sitethat is not affiliated with the medical picture archive system 2620. Thecentral server system 2640 can be a server configured to be outside thenetwork firewall and/or out outside the physical security of thehospital or other medical entity or otherwise not covered by theparticular administrative, physical and technical safeguards of thehospital or other medical entity.

FIG. 8C further illustrates how model parameters can be updated overtime to improve existing inference functions and/or to add new inferencefunctions, for example corresponding to new scan categories. Inparticular, the some or all of the de-identified medical scans generatedby the de-identification system 2608 can be transmitted back to thecentral server system, and the central server system 2640 can train onthis data to improve existing models by producing updated modelparameters of an existing inference function and/or to generate newmodels, for example, corresponding to new scan categories, by producingnew model parameters for new inference functions. For example, thecentral server system 2640 can produce updated and/or new modelparameters by performing the training step 1352 of the medical scanimage analysis system 112, as discussed in conjunction with FIG. 7A, ona plurality of de-identified medical scans received from the medicalpicture archive integration system 2600.

The image type parameters can be determined by the central server systemto dictate characteristics of the set of de-identified medical scans tobe received to train and/or retrain the model. For example, the imagetype parameters can correspond to one or more scan categories, canindicate scan classifier data 420, can indicate one or more scanmodalities, one or more anatomical regions, a date range, and/or otherparameters. The image type parameters can be determined by the centralserver system based on training parameters 620 determined for thecorresponding inference function to be trained, and/or based oncharacteristics of a new and/or existing scan category corresponding tothe inference function to be trained. The image type parameters can besent to the medical picture archive integration system 2600, and arequest such as a DICOM query can be sent to the medical picture archivesystem 2620, via transmitter 2604, that indicates the image typeparameters. For example, the processing system 2682 can be utilized togenerate the DICOM query based on the image type parameters receivedfrom the central server system 2640. The medical picture archive systemcan automatically transmit one or more DICOM images to the medicalpicture archive integration system in response to determining that theone or more DICOM images compares favorably to the image typeparameters. The DICOM images received in response can be de-identifiedby the de-identification system 2608. In some embodiments, thede-identified medical scans can be transmitted directly to the centralserver system 2640, for example, without generating annotation data.

The central server system can generate the new and/or updated modelparameters by training on the received set of de-identified medicalscans, and can transmit the new and/or updated model parameters to thede-identified storage system. If the model parameters correspond to anew inference function for a new scan category, the medical picturearchive integration system 2600 can generate a request, such as a DICOMquery, for transmission to the medical picture archive system indicatingthat incoming scans corresponding to image type parameters correspondingto the new scan category be sent to the medical picture archiveintegration system. The annotating system can update the set ofinference functions to include the new inference function, and theannotating system can select the new inference function from the set ofinference functions for subsequently generated de-identified medicalscans by the de-identification system by determining each of thesede-identified medical scans indicate the corresponding DICOM imagecorresponds to the new scan category. The new model parameters can beutilized to perform the new inference function on each of thesede-identified medical scans to generate corresponding annotation data,and an annotated DICOM file corresponding to each of these de-identifiedmedical scans can be generated for transmission to the medical picturearchive system via the transmitter.

In some embodiments, the central server system 2640 receives a pluralityof de-identified medical scans from a plurality of medical picturearchive integration system 2600, for example, each installed at aplurality of different hospitals or other medical entities, via thenetwork 2630. The central server system can generate training sets byintegrating de-identified medical scans from some or all of theplurality of medical picture archive integration systems 2600 to trainone or more inference functions and generate model parameters. Theplurality of medical picture archive integration systems 2600 canutilize the same set of inference functions or different sets ofinference functions. In some embodiments, the set of inference functionsutilized by the each of the plurality of medical picture archive systems2620 are trained on different sets of training data. For example, thedifferent sets of training data can correspond to the set ofde-identified medical scans received from the corresponding medicalpicture archive integration system 2600.

In some embodiments, the medical scan diagnosing system 108 can beutilized to implement the annotating system 2612, where thecorresponding subsystem processing device 235 and subsystem memorydevice 245 of the medical scan diagnosing system 108 are utilized toimplement the processing system 2682 and the memory 2684, respectively.Rather than receiving the medical scans via the network 150 as discussedin conjunction with FIG. 6A, the medical scan diagnosing system 108 canperform a selected medical scan inference function 1105 on an incomingde-identified medical scan generated by the de-identification system2608 and/or retrieved from the de-identified image storage system 2610.Memory 2684 can store the set of medical scan inference functions 1105,each corresponding to a scan category 1120, where the inference functionis selected from the set based on determining the scan category of thede-identified medical scan and selecting the corresponding inferencefunction. The processing system 2682 can perform the selected inferencefunction 1105 to generate the inference data 1110, which can be furtherutilized by the annotating system 2612 to generate the annotated DICOMfile for transmission back to the medical picture archive system 2620.New medical scan inference functions 1105 can be added to the set whencorresponding model parameters are received from the central serversystem. The remediation step 1140 can be performed locally by theannotating system 2612 and/or can be performed by the central serversystem 2640 by utilizing one or more de-identified medical scans andcorresponding annotation data sent to the central server system 2640.Updated model parameters can be generated by the central server system2640 and sent to the medical picture archive integration system 2600 asa result of performing the remediation step 1140.

The central server system 2640 can be implemented by utilizing one ormore of the medical scan subsystems 101, such as the medical scan imageanalysis system 112 and/or the medical scan diagnosing system 108, toproduce model parameters for one or more inference functions. Thecentral server system can store or otherwise communicate with a medicalscan database 342 that includes the de-identified medical scans and/orannotation data received from one or more medical picture archiveintegration systems 2600. Some or all entries of the medical scandatabase 342 can be utilized to as training data to produce modelparameters for one or more inference functions. These entries of themedical scan database 342 can be utilized by other subsystems 101 asdiscussed herein. For example, other subsystems 101 can utilize thecentral server system 2640 to fetch medical scans and/or correspondingannotation data that meet specified criteria. The central server system2640 can query the medical picture archive integration system 2600 basedon this criteria, and can receive de-identified medical scans and/orannotation data in response. This can be sent to the requestingsubsystem 101 directly and/or can be added to the medical scan database342 or another database of the database storage system 140 for access bythe requesting sub system 101.

Alternatively or in addition, the central server system 2640 can storeor otherwise communicate with a user database 344 storing user profileentries corresponding to each of a plurality of medical entities thateach utilize a corresponding one of a plurality of medical picturearchive integration systems 2600. For example, basic user datacorresponding to the medical entity can be stored as basic user data, anumber of scans or other consumption information indicating usage of oneor more inference functions by corresponding medical picture archiveintegration system can be stored as consumption usage data, and/or anumber of scans or other contribution information indicatingde-identified scans sent to the central server system as training datacan be stored as contribution usage data. The user profile entry canalso include inference function data, for example, with a list of modelparameters or function identifiers, such as medical scan analysisfunction identifiers 357, of inference functions currently utilized bythe corresponding medical picture archive integration system 2600. Theseentries of the user database 344 can be utilized by other subsystems 101as discussed herein.

Alternatively or in addition, the central server system 2640 can storeor otherwise communicate with a medical scan analysis function database346 to store model parameters, training data, or other information forone or more inference functions as medical scan analysis functionentries 356. In some embodiments, model parameter data 623 can indicatethe model parameters and function classifier data 610 can indicate thescan category of inference function entries. In some embodiments, themedical scan analysis function entry 356 can further include usageidentifying information indicating a medical picture archive integrationsystem identifier, medical entity identifier, and/or otherwiseindicating which medical archive integration systems and/or medicalentities have received the corresponding model parameters to utilize theinference function corresponding to the medical scan analysis functionentry 356. These entries of the medical scan analysis function database346 can be utilized by other subsystems 101 as discussed herein.

In some embodiments, the de-identification function is a medical scananalysis function, for example, with a corresponding medical scananalysis function entry 356 in the medical scan analysis functiondatabase 346. In some embodiments, the de-identification function istrained by the central server system 2640. For example, the centralserver system 2640 can send de-identification function parameters to themedical picture archive integration system 2600 for use by thede-identification system 2608. In embodiments with a plurality ofmedical picture archive integration systems 2600, each of the pluralityof medical picture archive integration systems 2600 can utilize the sameor different de-identification functions. In some embodiments, thede-identification function utilized by the each of the plurality ofmedical picture archive integration systems 2600 are trained ondifferent sets of training data. For example, the different sets oftraining data can correspond to each different set of de-identifiedmedical scans received from each corresponding medical picture archiveintegration system 2600.

In some embodiments, as illustrated in FIGS. 8D-8F, the medical picturearchive integration system 2600 can further communicate with a reportdatabase 2625, such as a Radiology Information System (RIS), thatincludes a plurality of medical reports corresponding to the DICOMimages stored by the medical picture archive system 2620.

As shown in FIG. 8D, the medical picture archive integration system 2600can further include a receiver 2603 that receives report data,corresponding to the DICOM image, from report database 2625. The reportdatabase 2625 can be affiliated with the medical picture archive system2620 and can store report data corresponding to DICOM images stored inthe medical picture archive system. The report data of report database2625 can include PHI, and the report database 2625 can thus bedisconnected from network 2630.

The report data can include natural language text, for example,generated by a radiologist that reviewed the corresponding DICOM image.The report data can be used to generate the de-identified medical scan,for example, where the de-identification system 2608 performs a naturallanguage analysis function on the report data to identify patientidentifying text in the report data. The de-identification system 2608can utilize this patient identifying text to detect matching patientidentifiers in the DICOM image to identify the patient identifiers ofthe DICOM image and generate the de-identified medical scan. In someembodiments, the report data can be de-identified by obfuscating,hashing, removing, replacing with a fiducial, or otherwise anonymizingthe identified patient identifying text to generate de-identified reportdata.

The de-identified report data can be utilized by the annotating system2612, for example, in conjunction with the DICOM image, to generate theannotation data. For example, the annotating system 2612 can perform anatural language analysis function on the de-identified natural languagetext of the report data to generate some or all of the annotation data.In some embodiments, the de-identified report data is sent to thecentral server system, for example, to be used as training data forinference functions, for natural language analysis functions, for othermedical scan analysis functions, and/or for use by at least one othersubsystem 101. For example, other subsystems 101 can utilize the centralserver system 2640 to fetch medical reports that correspond toparticular medical scans or otherwise meet specified criteria. Thecentral server system 2640 can query the medical picture archiveintegration system 2600 based on this criteria, and can receivede-identified medical reports in response. This can be sent to therequesting subsystem 101 directly, can be added to the medical scandatabase 342, a de-identified report database, or another database ofthe database storage system 140 for access by the requesting sub system101.

In some embodiments the medical picture archive integration system 2600can query the report database 2625 for the report data corresponding toa received DICOM image by utilizing a common identifier extracted fromthe DICOM image.

In some embodiments, the report data can correspond to a plurality ofDICOM images. For example, the report data can include natural languagetext describing a plurality of medical scans of a patient that caninclude multiple sequences, multiple modalities, and/or multiple medicalscans taken over time. In such embodiments, the patient identifying textand/or annotation data detected in the report data can also be appliedto de-identify and/or generate annotation data for the plurality ofDICOM images it describes. In such embodiments, the medical picturearchive integration system 2600 can query the medical picture archivesystem 2620 for one or more additional DICOM images corresponding to thereport data, and de-identified data and annotation data for theseadditional DICOM images can be generated accordingly by utilizing thereport data.

In some embodiments, as shown in FIG. 8E, the medical picture archivesystem 2620 communicates with the report database 2625. The medicalpicture archive system 2620 can request the report data corresponding tothe DICOM image from the report database 2625, and can transmit thereport data to the medical picture archive integration system 2600 via aDICOM communication protocol for receipt via receiver 2602. The medicalpicture archive system 2620 can query the report database 2625 for thereport data, utilizing a common identifier extracted from thecorresponding DICOM image, in response to determining to send thecorresponding DICOM image to the medical picture archive integrationsystem 2600.

FIG. 8F presents an embodiment where report data is generated by theannotating system 2612 and is transmitted, via a transmitter 2605, tothe report database 2625, for example via a DICOM communication protocolor other protocol recognized by the report database 2625. In otherembodiments, the report data is instead transmitted via transmitter 2604to the medical picture archive system 2620, and the medical picturearchive system 2620 transmits the report data to the report database2625.

The report data can be generated by the annotating system 2612 as outputof performing the inference function on the de-identified medical scan.The report data can include natural language text data 448 generatedautomatically based on other diagnosis data 440 such as abnormalityannotation data 442 determined by performing the inference function, forexample, by utilizing a medical scan natural language generatingfunction trained by the medical scan natural language analysis system114. The report data can be generated instead of, or in addition to, theannotated DICOM file.

FIG. 9 presents a flowchart illustrating a method for execution by amedical picture archive integration system 2600 that includes a firstmemory and a second memory that store executional instructions that,when executed by at least one first processor and at least one secondprocessor, respectfully, cause the medical picture archive integrationsystem to perform the steps below. In various embodiments, the firstmemory and at least one first processor are implemented by utilizing,respectfully, the memory 2654 and processing system 2652 of FIG. 8B. Invarious embodiments, the second memory is implemented by utilizing thememory 2674 and/or the memory 2684 of FIG. 8B. In various embodiments,the at least one second processor is implemented by utilizing theprocessing system 2682 of FIG. 8B.

Step 2702 includes receiving, from a medical picture archive system viaa receiver, a first DICOM image for storage in the first memory,designated for PHI, where the first DICOM image includes at least onepatient identifier. Step 2704 includes performing, via at least onefirst processor coupled to the first memory and designated for PHI, ade-identification function on the first DICOM image to identify the atleast one patient identifier and generate a first de-identified medicalscan that does not include the at least one patient identifier.

Step 2706 includes storing the first de-identified medical scan in asecond memory that is separate from the first memory. Step 2708 includesreceiving, via a network interface communicating with a network thatdoes not include the medical picture archive system, first modelparameters from a central server.

Step 2710 includes retrieving the first de-identified medical scan fromthe second memory. Step 2712 includes utilizing the first modelparameters to perform a first inference function on the firstde-identified medical scan to generate first annotation data via atleast one second processor that is different from the at least one firstprocessor. Step 2714 includes generating, via the at least one secondprocessor, a first annotated DICOM file for transmission to the medicalpicture archive system via a transmitter, where the first annotatedDICOM file includes the first annotation data and further includes anidentifier that indicates the first DICOM image. In various embodiments,the first annotated DICOM file is a DICOM presentation state file.

In various embodiments, the second memory further includes operationalinstructions that, when executed by the at least one second processor,further cause the medical picture archive integration system to retrievea second de-identified medical scan from the de-identified image storagesystem, where the second de-identified medical scan was generated by theat least one first processor by performing the de-identificationfunction on a second DICOM image received from the medical picturearchive system. The updated model parameters are utilized to perform thefirst inference function on the second de-identified medical scan togenerate second annotation data. A second annotated DICOM file isgenerated for transmission to the medical picture archive system via thetransmitter, where the second annotated DICOM file includes the secondannotation data and further includes an identifier that indicates thesecond DICOM image.

In various embodiments, the second memory stores a plurality ofde-identified medical scans generated by the at least one firstprocessor by performing the de-identification function on acorresponding plurality of DICOM images received from the medicalpicture archive system via the receiver. The plurality of de-identifiedmedical scans is transmitted to the central server via the networkinterface, and the central server generates the first model parametersby performing a training function on training data that includes theplurality of de-identified medical scans.

In various embodiments, the central server generates the first modelparameters by performing a training function on training data thatincludes a plurality of de-identified medical scans received from aplurality of medical picture archive integration systems via thenetwork. Each of the plurality of medical picture archive integrationsystems communicates bidirectionally with a corresponding one of aplurality of medical picture archive systems, and the plurality ofde-identified medical scans corresponds to a plurality of DICOM imagesstored by the plurality of medical picture archive integration systems.

In various embodiments, the first de-identified medical scan indicates ascan category of the first DICOM image. The second memory further storesoperational instructions that, when executed by the at least one secondprocessor, further cause the medical picture archive integration systemto select the first inference function from a set of inference functionsbased on the scan category. The set of inference functions correspondsto a set of unique scan categories that includes the scan category. Invarious embodiments, each unique scan category of the set of unique scancategories is characterized by one of a plurality of modalities and oneof a plurality of anatomical regions.

In various embodiments, the first memory further stores operationalinstructions that, when executed by the at least one first processor,further cause the medical picture archive integration system to receivea plurality of DICOM image data from the medical picture archive systemvia the receiver for storage in the first memory in response to a querytransmitted to the medical picture archive system via the transmitter.The query is generated by the medical picture archive integration systemin response to a request indicating a new scan category received fromthe central server via the network. The new scan category is notincluded in the set of unique scan categories, and the plurality ofDICOM image data corresponds to the new scan category. Thede-identification function is performed on the plurality of DICOM imagedata to generate a plurality of de-identified medical scans fortransmission to the central server via the network.

The second memory further stores operational instructions that, whenexecuted by the at least one second processor, further cause the medicalpicture archive integration system to receive second model parametersfrom the central server via the network for a new inference functioncorresponding to the new scan category. The set of inference functionsis updated to include the new inference function. The secondde-identified medical scan is retrieved from the first memory, where thesecond de-identified medical scan was generated by the at least onefirst processor by performing the de-identification function on a secondDICOM image received from the medical picture archive system. The newinference function is selected from the set of inference functions bydetermining the second de-identified medical scan indicates the secondDICOM image corresponds to the new scan category. The second modelparameters are utilized to perform the new inference function on thesecond de-identified medical scan to generate second annotation data. Asecond annotated DICOM file is generated for transmission to the medicalpicture archive system via the transmitter, where the second annotatedDICOM file includes the second annotation data and further includes anidentifier that indicates the second DICOM image.

In various embodiments, the medical picture archive integration systemgenerates parameter data for transmission to the medical picture archivesystem that indicates the set of unique scan categories. The medicalpicture archive system automatically transmits the first DICOM image tothe medical picture archive integration system in response todetermining that the first DICOM image compares favorably to one of theset of unique scan categories.

In various embodiments, the second memory further stores operationalinstructions that, when executed by the at least one second processor,cause the medical picture archive integration system to generate anatural language report data is based on the first annotation data andto transmit, via a second transmitter, the natural language report datato a report database associated with the medical picture archiveintegration system, where the natural language report data includes anidentifier corresponding to the first DICOM image.

In various embodiments, the first memory further stores operationalinstructions that, when executed by the at least one first processor,cause the medical picture archive integration system to receive, via asecond receiver, a natural language report corresponding to the firstDICOM image from the report database. A set of patient identifying textincluded in the natural language report are identified. Performing thede-identification function on the first DICOM image includes searchingthe first DICOM image for the set of patient identifying text toidentify the at least one patient identifier.

In various embodiments, the first memory is managed by a medical entityassociated with the medical picture archive system. The medical picturearchive integration system is located at a first geographic sitecorresponding to the medical entity, and the central server is locatedat a second geographic site. In various embodiments, the first memory isdecoupled from the network to prevent the first DICOM image thatincludes the at least one patient identifier from being communicated viathe network. In various embodiments, the medical picture archive systemis a Picture Archive and Communication System (PACS) server, and thefirst DICOM image is received in response to a query sent to the medicalpicture archive system by the transmitter in accordance with a DICOMcommunication protocol.

FIG. 10A presents an embodiment of a de-identification system 2800. Thede-identification system 2800 can be utilized to implement thede-identification system 2608 of FIGS. 8A-8F. In some embodiments, thede-identification system 2800 can be utilized by other subsystems tode-identify image data, medical report data, private fields of medicalscan entries 352 such as patient identifier data 431, and/or otherprivate fields stored in databases of the database memory device 340.

The de-identification system can be operable to receive, from at leastone first entity, a medical scan and a medical report corresponding tothe medical scan. A set of patient identifiers can be identified in asubset of fields of a header of the medical scan. A header anonymizationfunction can be performed on each of the set of patient identifiers togenerate a corresponding set of anonymized fields. A de-identifiedmedical scan can be generated by replacing the subset of fields of theheader of the medical scan with the corresponding set of anonymizedfields.

A subset of patient identifiers of the set of patient identifiers can beidentified in the medical report by searching text of the medical reportfor the set of patient identifiers. A text anonymization function can beperformed on the subset of patient identifiers to generate correspondinganonymized placeholder text for each of the subset of patientidentifiers. A de-identified medical report can be generated byreplacing each of the subset of patient identifiers with thecorresponding anonymized placeholder text. The de-identified medicalscan and the de-identified medical report can be transmitted to a secondentity via a network.

As shown in FIG. 10A, the de-identification system 2800 can include atleast one receiver 2802 operable to receive medical scans, such asmedical scans in a DICOM image format. The at least one receiver 2802 isfurther operable to receive medical reports, such as report data 449 orother reports containing natural language text diagnosing, describing,or otherwise associated the medical scans received by thede-identification system. The medical scans and report data can bereceived from the same or different entity, and can be received by thesame or different receiver 2802 in accordance with the same or differentcommunication protocol. For example, the medical scans can be receivedfrom the medical picture archive system 2620 of FIGS. 8A-8F and thereport data can be received from the report database 2625 of FIGS.8D-8F. In such embodiments, the receiver 2802 can be utilized toimplement the receiver 2602 of FIG. 8B.

The de-identification system 2800 can further include a processingsystem 2804 that includes at least one processor, and a memory 2806. Thememory 2806 can store operational instructions that, when executed bythe processing system, cause the de-identification system to perform atleast one patient identifier detection function on the received medicalscan and/or the medical report to identify a set of patient identifiersin the medical scan and/or the medical report. The operationalinstructions, when executed by the processing system, can further causethe de-identification system to perform an anonymization function on themedical scan and/or the medical report to generate a de-identifiedmedical scan and/or a de-identified medical report that do not includethe set of patient identifiers found in performing the at least onepatient identifier detection function. Generating the de-identifiedmedical scan can include generating a de-identified header andgenerating de-identified image data, where the de-identified medicalscan includes both the de-identified header and the de-identified imagedata. The memory 2806 can be isolated from Internet connectivity, andcan be designated for PHI.

The de-identification system 2800 can further include at least onetransmitter 2808, operable to transmit the de-identified medical scanand de-identified medical report. The de-identified medical scan andde-identified medical report can be transmitted back to the same entityfrom which they were received, respectively, and/or can be transmittedto a separate entity. For example, the at least one transmitter cantransmit the de-identified medical scan to the de-identified imagestorage system 2610 of FIGS. 8A-8F and/or can transmit the de-identifiedmedical scan to central server system 2640 via network 2630 of FIGS.8A-8F. In such embodiments, the transmitter 2808 can be utilized toimplement the interface 2655 of FIG. 8B. The receiver 2802, processingsystem 2804, memory 2806, and/or transmitter 2808 can be connected viabus 2810.

Some or all of the at least one patient identifier detection functionand/or at least one anonymization function as discussed herein can betrained and/or implemented by one or subsystems 101 in the same fashionas other medical scan analysis functions discussed herein, can be storedin medical scan analysis function database 346 of FIG. 3, and/or canotherwise be characterized by some or all fields of a medical scananalysis function entry 356 of FIG. 5.

The de-identification system 2800 can perform separate patientidentifier detection functions on the header of a medical report and/ormedical scan, on the text data of the medical report, and/or on theimage data of the medical scan, such as text extracted from the imagedata of the medical scan. Performance of each of these functionsgenerates an output of its own set of identified patient identifiers.Combining these sets of patient identifiers yields a blacklist term set.A second pass of the header of a medical report and/or medical scan, onthe text data of the medical report, and/or on the image data of themedical scan that utilizes this blacklist term set can catch any termsthat were missed by the respective patient identifier detectionfunction, and thus, the outputs of these multiple identificationprocesses can support each other. For example, some of the data in theheaders will be in a structured form and can thus be easier to reliablyidentify. This can be exploited and used to further anonymize theseidentifiers when they appear in free text header fields, report data,and/or in the image data of the medical scan. Meanwhile, unstructuredtext in free text header fields, report data, and/or image data of themedical scan likely includes pertinent clinical information to bepreserved in the anonymization process, for example, so it can beleveraged by at least one subsystems 101 and/or so it can be leveragedin training at least one medical scan analysis function.

At least one first patient identifier detection function can includeextracting the data in a subset of fields of a DICOM header, or anotherheader or other metadata of the medical scan and/or medical report witha known type that corresponds to patient identifying data. For example,this patient identifying subset of fields can include a name field, apatient ID number field or other unique patient identifier field, a datefield, a time field, an age field, an accession number field, SOPinstance UID, and/or other fields that could be utilized to identify thepatient and/or contain private information. A non-identifying subset offields of the header can include hospital identifiers, machine modelidentifiers, and/or some or all fields of medical scan entry 352 that donot correspond to patient identifying data. The patient identifyingsubset of fields and the non-identifying subset of fields can bemutually exclusive and collectively exhaustive with respect to theheader. The at least one patient identifier function can includegenerating a first set of patient identifiers by ignoring thenon-identifying subset of fields and extracting the entries of thepatient identifying subset of fields only. This first set of patientidentifiers can be anonymized to generate a de-identified header asdiscussed herein.

In some embodiments, at least one second patient identifier detectionfunction can be performed on the report data of the medical report. Theat least one second patient identifier detection function can includeidentifying patient identifying text in the report data by performing anatural language analysis function, for example, trained by the medicalscan natural language analysis system 114. For example, the at least onesecond patient identifier detection function can leverage the knownstructure of the medical report and/or context of the medical report. Asecond set of patient identifiers corresponding to the patientidentifying text can be determined, and the second set of patientidentifiers can be anonymized to generate a de-identified medicalreport. In some embodiments, a de-identified medical report includesclinical information, for example, because the portion of the originalmedical report that includes the clinical information was deemed to befree of patient identifying text and/or because the portion of theoriginal medical report that includes the clinical information wasdetermined to include pertinent information to be preserved.

In some embodiments, the medical report includes image datacorresponding to freehand or typed text. For example the medical reportcan correspond to a digitized scan of original freehand text written bya radiologist or other medical professional. In such embodiments, thepatient identifier detection function can first extract the text fromthe freehand text in the image data to generate text data before the atleast one second patient identifier detection function is performed onthe text of the medical report to generate the second set of patientidentifiers.

In some embodiments, the at least one second patient identifierdetection function can similarly be utilized to identify patientidentifying text in free text fields and/or unstructured text fields ofa DICOM header and/or other metadata of the medical scan and/or medicalreport data by performing a natural language analysis function, forexample, trained by the medical scan natural language analysis system114. A third set of patient identifiers corresponding to this patientidentifying text of the free text and/or unstructured header fields canbe determined, and the third set of patient identifiers can beanonymized to generate de-identified free text header field and/orunstructured header fields. In some embodiments, a de-identified freetext header field and/or unstructured header field includes clinicalinformation, for example, because the portion of the originalcorresponding header field that includes the clinical information wasdeemed to be free of patient identifying text and/or because the portionof the original corresponding header field that includes the clinicalinformation was determined to include pertinent information to bepreserved.

Patient identifiers can also be included in the image data of themedical scan itself. For example, freehand text corresponding to apatient name written on a hard copy of the medical scan beforedigitizing can be included in the image data, as discussed inconjunction with FIG. 10B. Other patient identifiers, such asinformation included on a patient wristband or other identifyinginformation located on or within the vicinity of the patient may havebeen captured when the medical scan was taken, and can thus be includedin the image. At least one third patient identifier detection functioncan include extracting text from the image data and/or detectingnon-text identifiers in the image data by performing a medical scanimage analysis function, for example, trained by the medical scan imageanalysis system 112. For example, detected text that corresponds to animage location known to include patient identifiers, detected text thatcorresponds to a format of a patient identifier, and/or or detected textor other image data determined to correspond to a patient identifier canbe identified. The at least one third patient identifier detectionfunction can further include identifying patient identifying text in thetext extracted from the image data by performing the at least one secondpatient identifier detection function and/or by performing a naturallanguage analysis function. A fourth set of patient identifierscorresponding to patient identifying text or other patient identifiersdetected in the image data of the medical scan can be determined, andthe fourth set of patient identifiers can be anonymized in the imagedata to generate de-identified image data of the medical scan asdescribed herein. In particular, the fourth set of patient identifierscan be detected in a set of regions of image data of the medical scan,and the set of regions of the image data can be anonymized.

In some embodiments, only a subset of the patient identifier detectionfunctions described herein are performed to generate respective sets ofpatient identifiers for anonymization. In some embodiments, additionalpatient identifier detection functions can be performed on the medicalscan and/or medical report to determine additional respective sets ofpatient identifiers for anonymization. The sets of patient identifiersoutputted by performing each patient identifier detection function canhave a null or non-null intersection. The sets of patient identifiersoutputted by performing each patient identifier function can have nullor non-null set differences.

Cases where the sets of patient identifiers have non-null setdifferences can indicate that a patient identifier detected by onefunction may have been missed by another function. The combined set ofpatient identifiers, for example, generated as the union of the sets ofsets of patient identifiers outputted by performing each patientidentifier function, can be used to build a blacklist term set, forexample, stored in memory 2806. The blacklist term set can designate thefinal set of terms to be anonymized. A second pass of header data,medical scans, medical reports, and/or any free text extracted from theheader data, the medical scan, and/or the medical report can beperformed by utilizing the blacklist term set to flag terms foranonymization that were not caught in performing the respective at leastone patient identifier detection function. For example, performing thesecond pass can include identifying at least one patient identifier ofthe blacklist term set in the header, medical report, and/or image dataof the medical scan. This can include by searching correspondingextracted text of the header, medical report, and/or image data forterms included in blacklist term set and/or by determining if each termin the extracted text is included in the blacklist term set.

In some embodiments, at least one patient identifier is not detecteduntil the second pass is performed. Consider an example where a freetext field of a DICOM header included a patient name that was notdetected in performing a respective patient identifier detectionfunction on the free text field of the DICOM header. However, thepatient name was successfully identified in the text of the medicalreport in performing a patient identifier detection function on themedical report. This patient name is added to the blacklist term list,and is detected in a second pass of the free text field of the DICOMheader. In response to detection in the second pass, the patient name ofthe free text field of the DICOM header can be anonymized accordingly togenerate a de-identified free text field. Consider a further examplewhere the patient name is included in the image data of the medicalscan, but was not detected in performing a respective patient identifierdetection function on the free text field of the DICOM header. In thesecond pass, this patient name can be detected in at least one region ofimage data of the medical scan by searching the image data for theblacklist term set.

In some embodiments, performing some or all of the patient identifierdetection functions includes identifying a set of non-identifying terms,such as the non-identifying subset of fields of the header. Inparticular, the non-identifying terms can include terms identified asclinical information and/or other terms determined to be preserved. Thecombined set of non-identifying terms, for example, generated as theunion of the sets of sets of non-identifying outputted by performingeach patient identifier function, can be used to build a whitelist termset, for example, stored in memory 2806. Performing the second pass canfurther include identifying at least one non-identifying term of thewhitelist term set in the header, medical report, and/or image data ofthe medical scan, and determining not to anonymize, or to otherwiseignore, the non-identifying term.

In various embodiments, some or all terms of the whitelist term set canbe removed from the blacklist term set. In particular, at least one termpreviously identified as a patient identifier in performing one or morepatient identifier detection functions is determined to be ignored andnot anonymized in response to determining the term is included in thewhitelist term set. This can help ensure that clinically importantinformation is not anonymized, and is thus preserved in thede-identified medical scan and de-identified medical report.

In some embodiments, the second pass can be performed after each of thepatient identifier detection functions are performed. For example,performing the anonymization function can include performing this secondpass by utilizing the blacklist term set to determine the final set ofterms to be anonymized. New portions of text in header fields, notpreviously detected in generating the first set of patient identifiersor the third set of patient identifiers, can be flagged foranonymization by determining these new portions of text correspond toterms of the blacklist term set. New portions of text the medicalreport, not previously detected in generating in the second set ofpatient identifiers, can be flagged for anonymization by determiningthese new portions of text correspond to terms of the blacklist termset. New regions of the image data of the medical scan, not previouslydetected in generating the fourth set of patient identifiers, can beflagged for anonymization by determining these new portions of textcorrespond to terms of the blacklist term set.

In some embodiments, the blacklist term set is built as each patientidentifier detection function is performed, and performance ofsubsequent patient identifier detection functions includes utilizing thecurrent blacklist term set. For example, performing the second patientidentifier detection function can include identifying a first subset ofthe blacklist term set in the medical report by searching the text ofthe medical report for the blacklist term set and/or by determining ifeach term in the text of the medical report is included in the blacklistterm set. Performing the second patient identifier detection functioncan further include identifying at least one term in the medical reportthat is included in the whitelist term set, and determining to ignorethe term in response. The first subset can be anonymized to generate thede-identified medical report as discussed herein. New patientidentifiers not already found can be appended to the blacklist term set,and the updated blacklist term set can be applied to perform a secondsearch of the header and/or image data of the medical scan, and at leastone of the new patient identifiers can be identified in the header inthe second search of the header and/or in the image data in a secondsearch of the image data. These newly identified patient identifiers inthe header and/or image data are anonymized in generating thede-identified medical scan.

As another example, a second subset of the blacklist term set can bedetected in a set of regions of image data of the medical scan byperforming the medical scan image analysis function on image data of themedical scan, where the image analysis function includes searching theimage data for the set of patient identifiers. For example, the medicalscan image analysis function can include searching the image data fortext, and the second subset can include detected text that matches oneor more terms of the blacklist term set. In some embodiments, detectedtext that matches one or more terms of the whitelist term set can beignored. The second subset can be anonymized to generate de-identifiedimage data as discussed herein. New patient identifiers that aredetected can be appended to the blacklist term set, and the updatedblacklist term set can be applied to perform a second search of theheader and/or metadata of the medical scan, and/or can be applied toperform a second search of the medical report. At least one of the newpatient identifiers can be identified in the header as a result ofperforming the second search of the header and/or at least one of thenew patient identifiers can be identified medical report as a result ofperforming the second search of the medical report. These newlyidentified patient identifiers can be anonymized in the header alongwith the originally identified blacklist term set in generating thede-identified header, and/or can be anonymized in the medical reportalong with the originally identified first subset in generating thede-identified medical report.

In some embodiments, the memory 2806 further stores a global blacklist,for example, that includes a vast set of known patient identifyingterms. In some embodiments, the global blacklist is also utilized by atleast one patient identifier detection function and/or in performing thesecond pass to determine patient identifying terms for anonymization. Insome embodiments, the blacklist term set generated for a particularmedical scan and corresponding medical report can be appended to theglobal blacklist for use in performing the second pass and/or indetecting patient identifiers in subsequently received medical scansand/or medical reports.

Alternatively or in addition, the memory 2806 can further store a globalwhitelist, for example, that includes a vast set of terms that can beignored. In particular, the global whitelist can include clinical termsand/or other terms that are deemed beneficial to preserve that do notcorrespond to patient identifying information. In some embodiments, theglobal whitelist is utilized by at least one patient identifierdetection function and/or in performing the second pass to determineterms to ignore in the header, image data, and/or medical report. Insome embodiments, the whitelist term set generated for a particularmedical scan and corresponding medical report can be appended to theglobal whitelist for use in performing the second pass and/or inignoring terms in subsequently received medical scans and/or medicalreports.

Alternatively or in addition, the memory 2806 can further store a globalgraylist, for example, that includes ambiguous terms that could bepatient identifying terms in some contexts, but non-identifying terms inother contexts. For example, “Parkinson” could correspond to patientidentifying data if part of a patient name such as “John Parkinson”, butcould correspond to non-patient identifying data meant to be ignored andpreserved in the de-identified medical report and/or de-identifiedmedical scan if part of a diagnosis term such as “Parkinson's disease.”In some embodiments, the global graylist is also utilized in performingthe second pass and/or in performing at least one patient identifierdetection function to determine that a term is included in the graylist,and to further determine whether the term should be added to theblacklist term set for anonymization or whitelist term set to be ignoredby leveraging context of accompanying text, by leveraging known datatypes of a header field from which the term was extracted, by leveragingknown structure of the term, by leveraging known data types of alocation of the image data from which the term was extracted, and/or byleveraging other contextual information. In some embodiments, thegraylist term set can be updated based on blacklist and/or whitelistterm sets for a particular medical scan and corresponding medicalreport.

In some embodiments, the at least one anonymization function includes afiducial replacement function. For example, some or all of the blacklistterm set can be replaced with a corresponding, global fiducial in theheader, report data, and/or image data. In some embodiments, the globalfiducial can be selected from a set of global fiducials based on a typeof the corresponding patient identifier. Each patient identifierdetected in the header and/or medical report can be replaced with acorresponding one of the set of global text fiducials. Each patientidentifiers detected in the image data can be replaced with acorresponding one of the set of global image fiducials. For example, oneor more global image fiducials can overlay pixels of regions of theimage data that include the identifying patient data, to obfuscate theidentifying patient data in the de-identified image data.

The global text fiducials and/or global image fiducials can berecognizable by inference functions and/or training functions, forexample, where the global text fiducials and global image fiducials areignored when processed in a training step to train an inference functionand/or are ignored in an inference step when processed by an inferencefunction. Furthermore, the global text fiducials and/or global imagefiducials can be recognizable by a human viewing the header, medicalreport, and/or image data. For example, a radiologist or other medicalprofessional, upon viewing a header, medical report, and/or image data,can clearly identify the location of a patient identifier that wasreplaced by the fiducial and/or can identify the type of patientidentifier that was replaced by the fiducial.

As an example, the name “John Smith” can be replaced in a header and/ormedical report with the text “% PATIENT NAME %”, where the text “%PATIENT NAME %” is a global fiducial for name types of the header and/orthe text of medical reports. The training step and/or inference step ofmedical scan natural language analysis functions can recognize andignore text that matches “% PATIENT NAME %” automatically.

FIG. 10B illustrates an example of anonymizing patient identifiers inimage data of a medical scan. In this example, the name “John Smith” andthe date “May 4, 2010” is detected as freehand text in the originalimage data of a medical scan. The regions of the image data that includethe patient identifiers can each be replaced by global fiducial in theshape of a rectangular bar, or any other shape. As shown in FIG. 10B, afirst region corresponding to the location of “John Smith” in theoriginal image data is replaced by fiducial 2820 in the de-identifiedimage data, and a second region corresponding to the location of “May 4,2010” in the original image data is replaced by fiducial 2822 in thede-identified image data. The size, shape, and/or location of eachglobal visual fiducial can be automatically determined based on thesize, shape, and/or location of the region that includes the patientidentifier to minimize the amount of the image data that is obfuscated,while still ensuring the entirety of the text is covered. While notdepicted in FIG. 10B, the fiducial can be of a particular color, forexample, where pixels of the particular color are automaticallyrecognized by the training step and/or inference step of medical scanimage analysis functions to indicate that the corresponding region beignored, and/or where the particular color is not included in theoriginal medical scan and/or is known to not be included in any medicalscans. The fiducial can include text recognizable to human inspectionsuch as “% PATIENT NAME” and “% DATE” as depicted in FIG. 10B, and/orcan include a QR code, logo, or other unique symbol recognizable tohuman inspection and/or automatically recognizable by the training stepand/or inference step of medical scan image analysis functions toindicate that the corresponding region be ignored.

In some embodiments, other anonymization functions can be performed ondifferent ones of the patient identifying subset of fields to generatethe de-identified header, de-identified report data, and/orde-identified image data. For example, based on the type of identifyingdata of each field of the header, different types of headeranonymization functions and/or text anonymization functions can beselected and utilized on the header fields, text of the report, and/ortext extracted from the image data. A set of anonymization functions caninclude a shift function, for example, utilized to offset a date, timeor other temporal data by a determined amount to preserve absolute timedifference and/or to preserve relative order over multiple medical scansand/or medical reports of a single patient. FIG. 10B depicts an examplewhere the shift function is performed on the date detected in the imagedata to generate fiducial 2822, where the determined amount is 10 yearsand 1 month. The determined amount can be determined by thede-identification system randomly and/or pseudo-randomly for eachpatient and/or for each medical scan and corresponding medical report,ensuring the original date cannot be recovered by utilizing a knownoffset. In various embodiments, other medical scans and/or medicalreports are fetched for the same patient by utilizing a patient IDnumber or other unique patient identifier of the header. These medialscans and reports can be anonymized as well, where the dates and/ortimes detected in these medical scans and/or medical reports offset bythe same determined amount, randomized or pseudo-randomized forparticular patient ID number, for example, based on performing a hashfunction on the patient ID number.

The set of anonymization functions can include at least one hashfunction, for example utilized to hash a unique patient ID such as apatient ID number, accession number, and/or SOP instance UID of theheader and/or text. In some embodiments, the hashed SOP instance UID,accession number, and/or patient ID number are prepended with a uniqueidentifier, stored in a database of the memory 2806 and/or shared withthe entities to which the de-identified medical scans and/or medicalreports are transmitted, so that de-identified medical scans and theircorresponding de-identified medical reports can be linked and retrievedretroactively. Similarly, longitudinal data can be preserved as multiplemedical scans and/or medical reports of the same patient will beassigned the same hashed patient ID.

The set of anonymization functions can further include at least onemanipulator function for some types of patient identifiers. Some valuesof header fields and/or report text that would normally not beconsidered private information can be considered identifying patientdata if they correspond to an outlier value or other rare value thatcould then be utilized to identify the corresponding patient from a verysmall subset of possible options. For example, a patient age over 89could be utilized to determine the identity of the patient, for example,if there are very few patients over the age of 89. To prevent suchcases, in response to determining that a patient identifier correspondsto an outlier value and/or in response to determining that a patientidentifier compares unfavorably to a normal-range threshold value, thepatient identifier can be capped at the normal-range threshold value orcan otherwise be manipulated. For example, a normal-range thresholdvalue corresponding to age can be set at 89, and generating ade-identified patient age can include capping patient ages that arehigher than 89 at 89 and/or can include keeping the same value forpatient ages that are less than or equal to 89.

In some embodiments, the de-identified header data is utilized toreplace the corresponding first subset of patient identifiers detectedin the medical report with text of the de-identified header fields. Inother embodiments, a set of text anonymization functions includes aglobal text fiducial replacement function, shift function, a hashfunction, and/or manipulator functions that anonymize the correspondingtypes of patient identifiers in the medical report separately.

In some embodiments where the image data of a medical scan includes ananatomical region corresponding to a patient's head, the image data mayinclude an identifying facial structure and/or facial features thatcould be utilized to determine the patient's identity. For example, adatabase of facial images, mapped to a corresponding plurality of peopleincluding the patient, could be searched and a facial recognitionfunction could be utilized to identify the patient in the database.Thus, facial structure included in the image data can be consideredpatient identifying data.

To prevent this problem and maintain patient privacy, thede-identification system can further be implemented to perform facialobfuscation for facial structure detected in medical scans. At least oneregion of the image data that includes identifying facial structure canbe determined by utilizing a medical image analysis function. Forexample, the medical image analysis function can include a facialdetection function that determines the regions of the image data thatinclude identifying facial structure based on searching the image datafor pixels with a density value that corresponds to facial skin, facialbone structure, or other density of an anatomical mass type thatcorresponds to identifying facial structure, and the facial obfuscationfunction can be performed on the identified pixels. Alternatively or inaddition, the facial detection function can determine the region basedon identifying at least one shape in the image data that corresponds toa facial structure.

The image obfuscation function can include a facial structureobfuscation function performed on the medical scan to generatede-identified image data that does not include identifying facialstructure. For example, the facial structure obfuscation function canmask, scramble, replace with a fiducial, or otherwise obfuscate thepixels of the region identified by the facial detection function. Insome embodiments, the facial structure obfuscation function can performa one-way function on the region that preserves abnormalities of thecorresponding portions of the image, such as nose fractures or facialskin legions, while still obfuscating the identifying facial structuresuch that the patient is not identifiable. For example, the pixels ofthe identifying facial structure can be altered such that they convergetowards a fixed, generic facial structure. In some embodiments, aplurality of facial structure image data of a plurality of patients canbe utilized to generate the generic facial structure, for example,corresponding to an average or other combination of the plurality offaces. For example, the pixels of the generic facial structure can beaveraged with, superimposed upon, or otherwise combined with the pixelsof the region of the image data identified by the facial detectionfunction in generating the de-identified image data.

In some embodiments, a hash function can be performed on an average ofthe generic facial structure and the identified facial structure of theimage data so that the generic facial structure cannot be utilized inconjunction with the resulting data of the de-identified image data toreproduce the original, identifying facial structure. In suchembodiments, the hash function can alter the pixel values while stillpreserving abnormalities. In some embodiments, a plurality of random,generic facial structures can be generated by utilizing the plurality offacial structure image data, for example, where each if the plurality offacial structure image data are assigned a random or pseudo-randomweight in an averaging function utilized to create the generic facialstructure, where a new, random or pseudo-random set of weights aregenerated each time the facial structure obfuscation function isutilized to create a new, generic facial structure to be averaged withthe identified facial structure in creating the de-identified image datato ensure the original identifying facial structure cannot be extractedfrom the resulting de-identified image data.

While facial obfuscation is described herein, similar techniques can beapplied in a similar fashion to other anatomical regions that aredetermined to include patient identifiers and/or to other anatomicalregions that can be utilized to extract patient identifying informationif not anonymized.

In some embodiments, the at least one receiver 2802 is included in atleast one transceiver, for example, enabling bidirectional communicationbetween the medical picture archive system 2620 and/or the reportdatabase 2625. In such embodiments, the de-identification system 2800can generate queries to the medical picture archive system 2620 and/orthe report database 2625 for particular medical scans and/or medicalreports, respectively. In particular, if the medical scan and medicalreport are stored and/or managed by separate memories and/or separateentities, they may not be received at the same time. However, a linkingidentifier, such as DICOM identifiers in headers or metadata of themedical scan and/or medical report, such accession number, patient IDnumber, SOP instance UID, or other linking identifier that maps themedical scan to the medical report can be utilized to fetch a medicalreport corresponding to a received medical scan and/or to fetch amedical scan corresponding to a received medical report via a query sentutilizing the at least one transceiver. For example, in response toreceiving the medical scan from the medical picture archive system 2620,the de-identification system can extract a linking identifier from aDICOM header of the medical scan, and can query the report database 2625for the corresponding medical report by indicating the linkingidentifier in the query. Conversely, in response to receiving themedical report from the report database 2625, the de-identificationsystem can extract the linking identifier from a header, metadata,and/or text body of the medical report, and can query the medicalpicture archive system 2620 for the corresponding medical scan byindicating the linking identifier in the query. In some embodiments, amapping of de-identified medical scans to original medical scans, and/ora mapping of de-identified medical reports to original medical reportscan be stored in memory 2806. In some embodiments, linking identifierssuch as patient ID numbers can be utilized to fetch additional medicalscans, additional medical reports, or other longitudinal datacorresponding to the same patient.

FIG. 11 presents a flowchart illustrating a method for execution by ade-identification system 2800 that stores executional instructions that,when executed by at least one processor, cause the de-identification toperform the steps below.

Step 2902 includes receiving from a first entity, via a receiver, afirst medical scan and a medical report corresponding to the medicalscan. Step 2904 includes identifying a set of patient identifiers in asubset of fields of a first header of the first medical scan. Step 2906includes performing a header anonymization function on each of the setof patient identifiers to generate a corresponding set of anonymizedfields. Step 2908 includes generating a first de-identified medical scanby replacing the subset of fields of the first header of the firstmedical scan with the corresponding set of anonymized fields. Step 2910includes identifying a first subset of patient identifiers of the set ofpatient identifiers in the medical report by searching text of themedical report for the set of patient identifiers. Step 2912 includesperforming a text anonymization function on the first subset of patientidentifiers to generate corresponding anonymized placeholder text foreach of the first subset of patient identifiers. Step 2914 includesgenerating a de-identified medical report by replacing each of the firstsubset of patient identifiers with the corresponding anonymizedplaceholder text. Step 2916 includes transmitting, via a transmitter,the de-identified first medical scan and the de-identified medicalreport to a second entity via a network.

In various embodiments, the medical scan is received from a PictureArchive and Communication System (PACS), where the medical report isreceived from a Radiology Information System (RIS), and where the firstde-identified medical scan and the de-identified medical report aretransmitted to a central server that is not affiliated with the PACS orthe MS. In various embodiments, first medical scan and the medicalreport are stored in a first memory for processing. The first memory isdecoupled from the network to prevent the set of patient identifiersfrom being communicated via the network. The first de-identified medicalscan and the de-identified medical report are stored in a second memorythat is separate from the first memory. The first de-identified medicalscan and the de-identified medical report are fetched from the secondmemory for transmission to the second entity.

In various embodiments, the header anonymization function performed oneach of the set of patient identifiers is selected from a plurality ofheader anonymization functions based on one of a plurality of identifiertypes of the corresponding one of the subset of fields. In variousembodiments, the plurality of identifier types includes a date type. Ashift function corresponding to the date type is performed on a firstdate of the first header to generate the first de-identified medicalscan, where the shift function includes offsetting the first date by adetermined amount. A second medical scan is received, via the receiver,that includes a second header. A unique patient ID of the first headermatches a unique patient ID of the second header. The shift function isperformed on a second date of the second header by offsetting the seconddate by the determined amount to generate a second de-identified medicalscan. The second de-identified medical scan is transmitted to the secondentity via the network.

In various embodiments, the plurality of identifier types includes aunique patient ID type. A hash function corresponding the unique patientID type is performed on the unique patient ID of the first header togenerate the first de-identified medical scan. The hash function isperformed on the unique patient ID of the second header to generate thesecond de-identified medical scan. An anonymized unique patient ID fieldof the first de-identified medical scan matches an anonymized uniquepatient ID field of the second de-identified medical scan as a result ofthe unique patient ID of the first header matching the unique patient IDof the second header.

In various embodiments, the plurality of identifier types includes alinking identifier type that maps the medical scan to the medicalreport. A hash function corresponding to the linking identifier type isperformed on a linking identifier of the first header to generate ahashed linking identifier. A linking identifier field of the firstde-identified medical scan includes the hashed linking identifier.Performing the text anonymization function on the first subset ofpatient identifiers includes determining one of the first subset ofpatient identifiers corresponds to linking identifier text andperforming the hash function on the one of the first subset of patientidentifiers to generate the hashed linking identifier, where thede-identified medical report includes the hashed linking identifier.

In various embodiments, a second subset of patient identifiers of theset of patient identifiers is identified in a set of regions of imagedata of the medical scan by performing an image analysis function onimage data of the medical scan. The image analysis function includessearching the image data for the set of patient identifiers. Anidentifier type is determined for each of the second subset of patientidentifiers. One of a plurality of image fiducials is selected for eachof the second subset of patient identifiers based on the identifiertype. De-identified image data is generated, where a set of regions ofthe de-identified image data, corresponding to the set of regions of theimage data, includes the one of the plurality of image fiducials toobfuscate each of the second subset of patient identifiers. Generatingthe first de-identified medical scan further includes replacing theimage data of the medical scan with the de-identified image data.

In various embodiments, a new patient identifier is identified in themedical report by performing a natural language analysis function on themedical report, where new patient identifier is not included in the setof patient identifiers. The set of patient identifiers is updated toinclude the new patient identifier prior to searching the image data ofthe medical scan for the set of patient identifiers, and the secondsubset of patient identifiers includes the new patient identifier.

In various embodiments, the memory further stores a global identifierblacklist. The natural language analysis function includes searching themedical report for a plurality of terms included in the globalidentifier blacklist to identify the new patient identifier. In variousembodiments, the de-identification system determines that the globalidentifier blacklist does not include one of the set of patientidentifiers, and the global identifier blacklist is updated to includethe one of the set of patient identifiers.

In various embodiments, performing the image analysis function furtherincludes identifying a new patient identifier in the image data, wherenew patient identifier is not included in the set of patientidentifiers. Identifying text is extracted from a region of the imagedata corresponding to the new patient identifier. The new patientidentifier is identified in the medical report by searching text of themedical report for the identifying text. The text anonymization functionis performed on new patient identifier to generate anonymizedplaceholder text for the new patient identifier. Generating thede-identified medical report further includes replacing the identifyingtext with the anonymized placeholder text for the new patientidentifier.

In various embodiments, generating the de-identified image data furtherincludes detecting an identifying facial structure in the image data ofthe medical scan. Generating the de-identified image data includesperforming a facial structure obfuscation function on the image data,and where the de-identified image data does not include the identifyingfacial structure.

FIGS. 12A-12C present an embodiment of a multi-label medical scananalysis system 3002. The multi-label medical scan analysis system canbe operable to train a multi-label model, and/or can utilize themulti-label model to generate inference data for new medical scans,indicating probabilities that each of a set of abnormality classes arepresent in the medical scan. Heat maps for each of the set ofabnormality can be generated based on probability matrices for displayto via a display device.

As shown in FIGS. 12A-12C, the multi-label medical scan analysis system3002 can communicate bi-directionally, via network 150, with the medicalscan database 342 and/or with other databases of the database storagesystem 140, with one or more client devices 120, and/or, while not shownin FIG. 12A, with one or more subsystems 101 of FIG. 1.

In some embodiments, the multi-label medical scan analysis system 3002is an additional subsystem 101 of the medical scan processing system100, implemented by utilizing the subsystem memory device 245, subsystemprocessing device 235, and/or subsystem network interface 265 of FIG.2A. For example, the multi-label medical scan analysis system 3002 canbe implemented by utilizing the medical scan image analysis system 112to train and/or utilize a computer vision model. In some embodiments,the multi-label medical scan analysis system 3002 utilizes, or otherwisecommunicates with, the central server system 2640. For example, themedical scan database 342 can be populated with de-identified datagenerated by the medical picture archive integration system 2600. Themulti-label medical scan analysis system 3002 can receive de-identifiedmedical scans of the training set with their corresponding annotationdata, diagnosis data, and/or medical reports directly from the medicalpicture archive integration system 2600, for example, where theannotation data, diagnosis data, and/or medical reports are utilized todetermine the medical labels for medical scans in the training set. Asanother example, the multi-label medical scan analysis system 3002 canperform an inference function on de-identified medical scans receivedfrom the medical picture archive integration system 2600, andprobability matrix data and/or determined abnormality classes generatedin the inference data can be assigned to the medical scan in the medicalpicture archive integration system 2600. As another example, themulti-label medical scan analysis system 3002 can request de-identifiedmedical scans, annotation data, and/or reports that match requestedcriteria for the training set and/or for new medical scans to belabeled. In some embodiments, some or all of the multi-label medicalscan analysis system 3002 is implemented by utilizing other subsystems101 and/or is operable to perform functions or other operationsdescribed in conjunction with one or more other subsystems 101.

As shown in FIG. 12A, the multi-label medical scan analysis system canbe operable to train a computer vision based model on a plurality ofmedical scans. A medical scan training set can be received from themedical scan database 342 and/or from another subsystem 101. The medicalscan training set can include a plurality of medical scans of the sameor different modality and/or anatomical region. For example, the medicalscan training set can include exclusively chest x-rays. The medical scantraining set can include a plurality of medical labels assigned to theplurality of medical scans. The medical labels assigned to a medicalscan can correspond to at least one of a set of abnormality classes, andeach medical scan in the training set can be labeled with zero, one, ora plurality of labels of the set of abnormality classes that are presentin the medical scan. For example, when the training set includes chestx-rays, the set of abnormality classes can include atelectasis,effusion, mass, pneumonia, consolidation, emphysema, pleural thickening,cardiomegaly, infiltration, nodule, pneumothorax, edema, fibrosis,and/or hernia. The abnormality classes can correspond to some or all ofthe abnormality classifier categories 444, and/or can correspond to anyset of abnormality types or categories, diagnosis types or categories,or medical conditions.

In some embodiments, the training set can further include region ofinterest data for some or all medical scans. The region of interest datacan indicate a portion of the medical scan and/or an anatomical regionwhere the medical label is present in the medical scan, and the modelcan be trained by utilizing this region of interest data. In otherembodiments, no region of interest information is provided, and the atleast one medical label assigned to a medical scan of the training datais considered a global label for the medical scan as a whole.

A training step 3010 can be applied to the medical scan training data togenerate model parameters or other model data corresponding to a trainedmodel. In some embodiments, multiple models are trained by utilizingmultiple sets of training data, for example, where each set of trainingdata corresponds to a different modality and/or anatomical region.Performing training step 3010 can include performing the training step1352 of the medical scan image analysis system 112. In some embodiments,performing the training step 3010 includes training a neural network,for example where the image data of each the plurality of medical scansis the input data. A vector corresponding to the set of abnormalityclasses, populated by binary indicators corresponding to which medicallabels correspond to each of the plurality of medical scans, cancorrespond the output data.

The data of the training set can alternatively correspond to a N×N×Kmatrix, where the value of N corresponds to a highest-resolution levelof a multi-resolution model, and where the value of K corresponds tonumber of abnormality classes in the set of abnormalities/abnormalityclasses. Each of the N×N values for each of the K classes can correspondto an image patch of two-dimensional image data, such as an x-ray orother two-dimensional medical image. When no region of interest data isavailable, each of the N×N values for each of the K classes can bepopulated with a binary indicator corresponding to whether thecorresponding abnormality class is present in the image data as a whole,where each of the N×N values for the same K class are assigned to thesame binary indicator. In the case where region of interest data isavailable in training, each of the N×N values for each of the K classescan be populated with a binary indicator corresponding to whether thecorresponding abnormality class is present in the corresponding imagepatch of the image data.

In embodiments where the medical scans of the training set have aplurality of slices, an additional dimension M can correspond to thenumber of slices, where the output data of the training set correspondsto a N×N×M×K matrix, where each of the N×N×M values for each of the Kclasses corresponds to an image patch of the corresponding image slice.

In some embodiments, other information of the medical scan accompanyingthe image data can be utilized as input data. For example, patientdemographic and/or history data, and/or other fields of a correspondingmedical scan entry 352 can be utilized to train the model along with theimage data.

The model data generated by performing the training step 3010 cancorrespond to a multi-resolution model such as the model described inFIG. 14A. The model data can be transmitted to other subsystems 101 foruse in implementing an inference function on new medical scans, can betransmitted to medical scan analysis function database 346 for access byone or more other subsystems 101, and/or can be stored locally by themulti-label medical scan analysis system 3002 for use in implementing aninference function on new medical scans.

As shown in FIG. 12B and FIG. 12C, the multi-label medical scan analysissystem 3002 can receive new medical scans from the medical scan database342 and/or from another subsystem 101. The multi-label medical scananalysis system 3002 can perform an inference function 3020 on the newmedical scans by utilizing the model data generated as discussed inconjunction with FIG. 12A. The inference function can be performed onthe image data alone, or can be performed on additional informationalong with the image data, such as metadata of the medical scan, patientdemographic and/or history data, and/or other fields of a correspondingmedical scan entry 352, for example, if the model was trained utilizingsuch information.

The output of the inference function 3020 can include a N×N×K matrixthat indicates N×N probability values for each of the K classes. Each ofthe N×N values, for each of the K classes, can correspond to one of N×Nimage patches of the image data. Each of the N×N probability values foreach of the K classes can correspond to a probability that the acorresponding one of the set of abnormality classes is present in thecorresponding image patch. As discussed in conjunction with FIG. 12A,the value of N can correspond to a resolution of a plurality ofresolution layers of the model. The patches can correspond to gridsquares in the image data. For example, if the size of the image data ofthe new medical scan is 1024×1024 pixels, the value of N can correspondto 64 for a total of 64² image patches, each containing 16×16 pixels. Inother examples, the patches can correspond to a plurality ofnon-disjoint subsets of the image data.

In some embodiments, the model data corresponds to a multi-resolutionmodel consisting of multiple resolution layers, where low resolutionlayers capture context and/or semantic information, and high resolutionlayers capture liner details. In particular, the model can include a setof resolution layers, each with a different value n×n×K, where the valueof n×n corresponds to the number of image patches the image ispartitioned into at that resolution layer. Thus, higher values of n cancorrespond to higher-resolution of the layer. The set of resolutionlayers can include an 8×8 layer, a 16×16 layer, a 32×32 layer and/or a64×64 layer, and the 64×64 layer can correspond to the output layer.

In some embodiments, resolution of the input image data is reduced tolower and lower resolutions, for example, using ResNets. Resolution ateach layer is preserved, for example, using DenseNets, to produce aninitial feature map at each layer. Final feature maps can be generatedat each layer, starting at the lowest resolution layer, and upsamplingthe results to generate the final feature maps at higher resolutionlayers. In particular, resolution preserving nonlinear transformationscan be incrementally applied be on a channel-wise concatenation of aprevious feature map of the current layer and the upsampled feature mapof the previous layer, for example, as discussed in conjunction withFIG. 13A. At the final, highest resolution layer, a probability matrixcan be generated by applying a sigmoid function to the final feature mapof the highest resolution layer, indicating probabilities for eachinstance (at 64×64 resolution), for each of the K classes. Spatialinformation is preserved by utilizing this coarse-to-fine approach,where at lower resolutions, weak localization cues can be furtherrefined in subsequent higher resolutions. In particular, thispreservation of spatial information allows abnormalities to be localizedin inference step 3020 based on the output probability matrix, even inembodiments where no region of interest data was utilized in trainingstep 3010.

In embodiments where the medical scans of the training set have aplurality of slices, an additional dimension M can correspond to thenumber of slices, where the output data of the training set correspondsto a N×N×M×K matrix, where each of the N×N×M values for each of the Kclasses corresponds to an image patch of the corresponding image slice.The value of M can be constant across each of the plurality ofresolution layers of the model, where there is a plurality of n×n imagepatches for each of the M slices at each of the resolution layers. Inother embodiments, the plurality of image patches are three-dimensionalimage subregions at some or all of the resolution layers, where thethree-dimensional image subregions include pixels from multiple imageslices. The slice-wise resolution can also improve with each resolutionlayer, for example, where the number of slices in each three-dimensionalimage subregion decreases at higher resolution layers. The number ofslices in each three-dimensional image subregion at the highestresolution layer can be equal to one, or can be equal to a differentlowest number.

In some embodiments, performing the inference function 3020 can includeperforming the inference step 1354 of FIG. 7B to generate one or moreprobability matrices 1371, with an added dimension K corresponding toeach of the plurality of abnormality classes. Thus, the probabilitymatrices 1371 can be generated for each of the K abnormality classes.The detection step 1372, abnormality classification step 1374, similarscan identification step 1376, and/or display parameter step 1378 cansimilarly be performed separately for each of the K abnormality classes.

The probability matrix data generated as output of the inferencefunction 3020 can be transmitted to the client device 120 for displayvia a display device. Alternatively or in addition, the probabilitymatrix data can be transmitted for use by another subsystem 101 and/orcan be transmitted to the medical scan database to be mapped to thecorresponding medical scan. Alternatively or in addition, theprobability matrix data can be utilized by the multi-label medical scananalysis system 3002 to generate saliency maps, to generate region ofinterest data, to generate global probabilities for each class asillustrated in FIG. 12B, and/or to generate heat map visualization dataas illustrated in FIG. 12C. As used herein a saliency map is an imagethat shows unique quality, such as an indication of probability on apixel-by-pixel or patch-by-patch basis. A saliency map can simplifyand/or change the representation of an image into something that is moremeaningful and easier to analyze.

Generating the saliency maps can include, for example, assigning eachvalue in the probability matrix to 1 or 0, and can be generated for eachresolution level. Each value in the probability matrix can be assignedthe value 1 or 0 by comparing the raw probability to a threshold. Thethreshold can be the same or different for each of the K abnormalityclasses, and/or can be the same or different for each of the N×N imagepatch locations. The saliency maps can be visually presented, forexample, where image patches assigned a value of 1 are displayed inwhite or another first color, and where image patches assigned a valueof 0 are displayed in black or another second color or color intensity.The saliency maps can be transmitted to the client device 120 fordisplay via a display device. Alternatively or in addition, the saliencymaps can be transmitted for use by another subsystem 101 and/or can betransmitted to the medical scan database to be mapped to thecorresponding medical scan.

Generating the region of interest data can include comparing values ofeach probability matrix to a probability threshold. The probabilitythreshold can be the same or different for the plurality of abnormalityclasses. In some embodiments, the region of interest data indicates oneor more image patches with a probability value that compared favorablyto the probability threshold for at least one of the K abnormalityclasses. In some embodiments, the at least one of the K abnormalityclasses for which the probability value compared favorably to theprobability threshold is further indicated in the region of interestdata. The region of interest data can be transmitted for use by anothersubsystem 101 and/or can be transmitted to the medical scan database tobe mapped to the corresponding medical scan. Alternatively or inaddition, the region of interest data can be transmitted to the clientdevice 120 for display via a display device. In particular, the regionof interest data can be displayed in conjunction with the medical scan,for example, where the one or more image patches identified in theregion of interest data are outlined, highlighted, or otherwiseindicated visually. Furthermore, the at least one of the abnormalityclasses corresponding to the region of interest can be identified astext, and/or as a color or pattern used to identify the region interest.For example, for region of interest data of a medical scan with a firstregion of interest corresponding to a first abnormality class and asecond region of interest corresponding to a second abnormality class,the first region of interest can be indicated via an interface displayedby the display device by overlaying the corresponding image patch of theimage data of the medical scan with a first color and/or pattern, andthe second region of interest can be indicated via the interface byoverlaying the corresponding image patch of the image data of theMedical scan with a second color and/or pattern. In some embodiments,the region of interest data is presented by utilizing the interface ofthe medical scan assisted review system 102. In some embodiments, theregion of interest data is presented by the interface of the multi-labelheat map display system of FIGS. 13A-13C.

FIG. 12B illustrates an embodiment of a multi-label medical scananalysis system 3002 that is implemented as a global multi-labelgenerating system. The multi-label medical scan analysis system 3002 cangenerate global probabilities, for example, by performing a medicallabeling function 3030 on the probability matrix data. Generating theglobal probabilities can include evaluating the N×N probability matrixfor each of the K classes and determining a global probability value foreach of the classes. In some embodiments, the global probability valuefor a given abnormality class is assigned a highest probability of thecorresponding N×N probability matrix. In some embodiments, the globalprobability value for an abnormality class is based on an average ofsome or all of the probability values of the corresponding N×Nprobability matrix, and/or based on some other function of the values ofthe N×N probability matrix. In some embodiments, determining the globalprobability value includes applying a filter to the probability matrix.

In some embodiments, a final binary identifier is assigned for each ofthe K classes, indicating whether each of the K abnormality classes aredetermined to be present or absent in the medical scan, based on theirglobal probabilities. For example, each of the K global probabilitiescan be compared to a probability threshold, which can be the same ordifferent for each of the K abnormality classes. The abnormality can bedetermined to be present when the corresponding global probability valuecompares favorably to the probability threshold, and can be determinedto be absent when the corresponding probability value comparesunfavorably to the probability threshold.

The global probability data and/or final binary identifiers determinedfor some or all of the K classes can be transmitted to the client device120 for display via a display device. Alternatively or in addition, theglobal probability data and/or final binary identifiers are transmittedfor use by another subsystem 101 and/or are transmitted to the medicalscan database to be mapped to the corresponding medical scan. In someembodiments a single, global binary identifier is generated,alternatively or in addition for the final binary identifiers for eachof the K classes. The global binary identifier can be generated based onthe probability matrix data, the global probability values, and/or finalbinary identifiers, and can indicate whether or not the medical scan isdetermined to include any abnormalities.

In some embodiments, the K abnormality classes are treated asindependent variables. In such cases, the global probability for eachabnormality class can be computed based only on corresponding the N×Nmatrix, independent of other N×N probability matrixes of the other K−1abnormality classes. Similarly, the final binary identifier for each ofthe abnormality classes can be computed based only on the correspondingglobal probability, independent of the global probabilities of the otherK−1 abnormality classes.

In other embodiments, dependency of some or all of the K abnormalityclasses is utilized in computing the global probabilities and/or thefinal binary identifiers. For example, correlation data that indicatescorrelations between pairs and/or subsets of the K abnormality classescan be determined and/or learned based on the training data, and theglobal probabilities and/or binary identifiers can be generated based onthe correlation data. For example, the global probabilities can becomputed as joint probabilities utilizing the correlation data. Asanother example, a global probability value for a first abnormalityclass can be set to a higher value in response to a high globalprobability value being computed for a second abnormality class that hasa high correlation with the first abnormality class. In someembodiments, the correlations are learned and inherently integratedwithin the model and are reflected when the probability matrices aregenerated as output when the inference function is performed, andfurther consideration of abnormality class interdependencies is notnecessary.

In various embodiments, a multi-label medical scan analysis system 3002implemented as a global multi-label generating system is operable toreceive, via a receiver, a plurality of medical scans and a plurality ofmedical labels corresponding to the plurality of medical scans. Each ofthe plurality of medical labels corresponds to one of a set ofabnormality classes. A computer vision model is generated by training onthe plurality of medical scans and the plurality of medical labels.Probability matrix data is generated by performing an inference function3020 that utilizes the computer vision model on a new medical scan. Theprobability matrix data includes, for each of a set of image patches ofthe new medical scan, a set of patch probability values corresponding tothe set of abnormality classes. Each of the set of patch probabilityvalues indicates a probability that a corresponding one of the set ofabnormality classes is present in the each of the set of image patches.Global probability data is generated based on the probability matrixdata. The global probability data indicates a set of global probabilityvalues corresponding to the set of abnormality classes, and each of theset of global probability values indicates a probability that acorresponding one of the set of abnormality classes is present in thenew medical scan. The global probability data is transmitted, via atransmitter, to a client device for display via a display device. Itshould be noted that the global probability value can indicate aprobability by being the probability itself, one minus the probability,the value of some other deterministic function of the probability, thevalue of some other likelihood function including a non-parametricstatistic, the value of another function or scale indicating a degree ofbelief or other value.

FIG. 12C illustrates an embodiment of the multi-label medical scananalysis system 3002 implemented as a multi-label heat map generatingsystem. Preliminary heat map visualization data is generated, forexample, by performing a heat map generator function 3040 on theprobability matrix data.

In particular, the preliminary heat map visualization data can indicatea preliminary heat map for each of the K abnormality classes, based ontheir corresponding N×N probability matrix. The preliminary heat mapvisualization data can assign, for each one of the set of K abnormalityclasses, one color value of a set of color values for portions of thenew medical scan where the one of the set of abnormality classes ispresent. Each preliminary heat map can indicate pixel values or othercolor values, corresponding to grayscale and/or RGB color values,corresponding to pixels of the input image data. In some embodiments,pixel values and/or other color values are only indicated for each imagepatch of the highest resolution, for example, where a single color valueis computed for each of the values of the N×N probability matrix foreach of the K classes. In some embodiments, preliminary heat maps aregenerated for each of the resolution layers, for example, where a singlecolor value is computed for each image patch of the correspondingresolution layer.

The pixel values of each preliminary heat map can be proportional to theraw probability values of the N×N probability and/or can be computed asa deterministic function of the raw probability values. In variousembodiments, the preliminary heat map visualization data has anintensity that is a function of the model confidence for each of the setof K abnormalities, with areas of greater confidence/probability havinga higher color intensity/brightness as opposed to areas of lowerconfidence/probability having a lower color intensity/brightness andfurther with areas of zero or substantially zero probability being dark.In addition or in the alternative, the colors assigned to the variousabnormalities can be predetermined based on the abnormality severityand/or a confidence that an abnormality is malignant/severe as opposedto being benign/harmless. For example, fungal disease can be color-codeddifferently than soft tissue.

In various embodiments, a color spectrum is used to assign colors basedon condition severity. More severe conditions can be assigned colors ona high end of the color spectrum such as red or orange with less severeconditions being assigned colors on the low end of the spectrum such asgreen or blue. For example, benign calcified granuloma can be coloredblue, while potentially cancerous abnormalities are colored red.Furthermore, nodules can be segmented/highlighted with a color spectrumas a function of how confident the model is that they are cancerousand/or based on, for example, a lung-RADS score calculated for eachnodule. In this fashion, benign nodules are also colored based on thelow end of the color spectrum, indicating that they were caught by themodel, but the model is confident that they are benign.

In various embodiment, the preliminary heat map visualization dataresults in a N×N resolution heat map, and if displayed by the samenumber of pixels as the input image, results in all pixels of the sameimage patch being assigned the same color and intensity. This can resultin borders between image patches having a dramatic shift in colorintensity. The heat map visualization data can be generated via heat-mappost-processing function 3060 by a post-processing of the preliminaryheat map visualization data to mitigate heat map artifacts. Post-processheat maps displayed to users can improve the technology of medical imagereviewing tools by more quickly drawing the user's attention to theregions of interest and/or emphasizing areas of most concern to thepatient. In particular, post-processing techniques can target the user'sattention to the right part of the scan, while maintaining the user'sconfidence in the underlying AI model and not confusing users based onextraneous findings that might be included in the preliminary heat mapvisualization data. Furthermore, post-processing can facilitate a betteruser experience or convey certain information instead of linking thelevel-of-heat directly to the model's indication of probability.

For a more visually desirable heat map, the post-processing function3060 can apply a smoothing function to smooth the color intensitytransitions between image patches and/or to otherwise soften the bordersbetween image patches by changing color intensity values graduallywithin each image patch in the direction towards each of up to fourborders, based on the color intensity of the neighboring image patch ineach of the up to four directions. For example, when a 64×64 dimensionprobability matrix is outputted for a 1024×1024 image for eachabnormality class, smoothing techniques can be applied within each ofthe 16×16 dimension patches, for example, to smooth the borders betweenpatches. In this case, the same image patch can include pixels ofvarying color intensities. Color intensity value differentials betweenones of a set of initial color values of neighboring pixels included indifferent ones of the set of image patches can be reduced as a result ofapplying the smoothing function. In some embodiments, the smoothingfunction is different for some or all of the K abnormality classes.

Alternatively or in addition, the heat map post-processing function 3060can apply a segmentation masking function to some or all of thepreliminary heat map visualization data to mask one or more designatedor determined regions, for example, based on borders of an anatomicalregion. For example, the segmentation masking function can maskeverything outside of the heart. Masking can include not assigning pixelvalues for the masked region of the heat map and/or can include settingthe pixel values for the masked region of the heat map to a mask colorsuch as black, white, or other uniform, predetermined mask color. Insome embodiments, the segmentation masking function is different forheat maps of each of the K different abnormality classes, for example,based on predetermined anatomical regions the different ones of the Kabnormality classes pertain to. For example, for chest x-rays, heat mapsfor some of the K abnormality classes can mask regions outside thelungs, for example, when the corresponding abnormality class isprevalent in the lungs. As a further example, other ones of the Kabnormality classes can mask regions outside the heart, for example,when the corresponding abnormality class is prevalent in the heart.

The heat map post-processing function 3060 can operate by: comparing theprobability that the corresponding one of the set of abnormality classesis present in the each of the set of image patches with a correspondingthreshold probability; and only highlight/color an image patch in theset of image patches when the probability compares favorably to theprobability threshold. These threshold values may be the same ordifferent for each of the set of K abnormalities. In addition or in thealternative, the heat map post-processing function 3060 can apply otherpost-processing techniques to the preliminary heat map visualizationdata. For example, a border detection function can be applied todetermine boundaries/borders corresponding to the portions/regions inthe preliminary heat map visualization data where one of abnormalitiesis present with high confidence, for example with probabilities above apredetermined threshold value.

In various embodiments, the heat map post-processing function 3060 canapply a smoothing function to the boundaries to the heat mappost-processing function 3060 to filter the boundaries in order toreduce tails, pixelization artifacts, sharp edges and/or otherirregularities and artifacts. In addition or in the alternative, theheat map post-processing function 3060 can apply a Gaussian blur orother blurring function to a border, apply a gradient blur or othersmoothing to the pixels in the detected region and/or apply filtering tooutlier points outside of the boundaries of the region in thepreliminary heat map visualization data and/or apply a segmentation maskto mask pixels of the preliminary heat map visualization data that areoutside of the boundaries, wherein the pixels that are outside theboundaries are not assigned color values in the heat map visualizationdata.

This heat map visualization data can be transmitted to a client device120 for display via an associated display device. In some embodiments,an interface is displayed by the display device in accordance with themedical scan report review system 102, medical scan report labelingsystem 104, medical scan annotating system 106, medical scan diagnosingsystem 108, other medical scan subsystem 101, a PACS viewing tool,and/or other medical scan viewer. This interface can be an interactiveuser interface that allows a user to interact with the interface andfurther that displays, based on the heat map visualization data, atleast one of the heat maps for a corresponding one of the K abnormalityclasses. Each heat map can be displayed based on the corresponding colorvalues for each pixel. In some embodiments, the raw pixel values of theheat map are displayed. In some embodiments, the heat map visualizationdata is superimposed on or overlaid on the medical scan image data via atransparency function, where features of the original image data arestill visible and the heat map highlights or shades the original imagedata in accordance with the color values/intensities of the heat mapvisualization data. The heat maps can be displayed in conjunction withtext identifying the corresponding abnormality class, with thecalculated global probability value for the corresponding abnormalityclass, and/or with the final binary identifier of the correspondingabnormality class. In various embodiments, the interface described inconjunction with PACS viewing tool, medical scan report review system102, medical scan report labeling system 104, medical scan annotatingsystem 106, medical scan diagnosing system 108, other medical scansubsystem 101 and/or other medical scan viewer.

In various embodiments, a multi-label medical scan analysis system 3002implemented as a multi-label heat map generating system is operablereceive, via a receiver, a plurality of medical scans and a plurality ofmedical labels corresponding to the plurality of medical scans, whereeach of the plurality of medical labels correspond to one of a set ofabnormality classes. A computer vision model can be generated bytraining on the plurality of medical scans and the plurality of medicallabels. A new medical scan can be received, via the receiver.Probability matrix data can be generated by performing an inferencefunction that utilizes the computer vision model on the new medicalscan. The probability matrix data includes, for each of a set of imagepatches of the new medical scan, a set of patch probability valuescorresponding to the set of abnormality classes. Each of the set ofpatch probability values indicates a probability that a correspondingone of the set of abnormality classes is present in the each of the setof image patches. Preliminary heat map visualization data can begenerated based on the probability matrix data. Heat map visualizationdata can be generated by a post-processing of the preliminary heat mapvisualization data to mitigate heat map artifacts. The heat mapvisualization data can indicate, for each of the set of abnormalityclasses, a different color value for pixels of the new medical scan thatcorrespond to a detected abnormality. The heat map visualization datacan be transmitted to the client device 120 for display via anassociated display device.

In various embodiments, an interface displayed by the display devicedisplays each of a set of heat maps indicated in the heat mapvisualization data, wherein each of the set of heat maps corresponds tothe set of abnormality classes. The multi-label heat map generatingsystem can further operate by generating global probability data basedon the probability matrix data, wherein the global probability dataindicates a set of global probability values corresponding to the set ofabnormality classes, wherein each of the set of global probabilityvalues indicates a probability that a corresponding one of the set ofabnormality classes is present in the new medical scan and further bygenerating heat map ordering data by ranking the global probabilitydata, wherein the interface displays the set of heat maps in an orderindicated by the heat map ordering data. For example, the ordering canbe established so that one of the set of heat maps corresponding to oneof the set of abnormality classes with a highest corresponding globalprobability value is displayed first.

The multi-label heat map generating system can further operate bydetermining a subset of the set of abnormality classes are present inthe new medical scan based on the probability matrix data. The interfacecan be configured to only display heat maps corresponding to the subsetof the set of abnormality classes. The interface used by the clientdevice 120 can further operate to respond to user interactions with theinterface to toggle between displays of two or more of the set of heatmaps, to respond to user interactions with the interface to select asubset of the set of heat maps to be displayed in association with afuture processing of another new medical scan by the multi-label heatmap generating system, and/or respond to user interactions with theinterface to select the custom heat map settings that are sent to viathe network 150. The custom heat map settings can be used, for example,by the heat map post-processing function 3060 to customize the heat mapvisualization data based on these a selection of probability thresholds,masks, region boundary processing and/or other post processingparameters. The custom heat map settings can also be used by the clientdevice 120, for example, to select particular modes for displaying theheat maps and/or other display parameters. In this fashion, for example,a radiologist/user can toggle between different heat map views,otherwise choose which heat maps to view and/or customize which heatmaps settings they prefer for incoming scans.

Factor data received via network 150 can also be used by heat mappost-processing function 360 to automatically set or adjust custom heatmap settings and/or used by the interface of client device 120 tosuggest particular custom heat map settings for differentradiologists/users based on the cognitive factors and/or systematicfactors determined to be prevalent in errors for these differentradiologists/users. In particular, the multi-label heat map generatingsystem can further operate by receiving factor data that identifies oneor more factors that contribute to errors associated with the medicalprofessional; and to determining the custom heat map setting, based onthe one or more factors that contribute to errors associated with themedical professional. The one or more factors can include at least onesystematic factor that indicates errors that occur more frequently forreviews by the medical professional associated with: using a particularone of plurality of viewing tools; using a particular interface featureof a viewing tool; a particular time a day; after a number of priorreviews in a reviewing session of the medical profession; and/or after aparticular duration of the reviewing session. In addition or in thealternative, the one or more factors can include one or more of: ananchoring bias factor; a framing bias factor; a satisfaction of searchfactor; a satisfaction of report factor; and/or a tunnel vision factor.

It should be noted that while the client device 120 and the multi-labelscan analysis system 3002 are shown as being separate, in variousembodiments, the client device 120 and the multi-label scan analysissystem 3002 can be implemented via a single subsystem 101 or othercomputer, processing module or other processing platform.

The discussion that follows in conjunction with FIGS. 13A-13D introducesan example embodiment of the model generated by multi-label medical scananalysis system 3002. Diagnostic imaging often requires the simultaneousidentification of a multitude of findings of varied size and appearance.Beyond global indication of said findings, the prediction and display oflocalization information improves trust in and understanding of resultswhen augmenting clinical workflow. Medical training data rarely includesmore than global image-level labels as segmentations are time-consumingand expensive to collect. The example embodiment of the multi-labelmedical scan analysis system 3002 utilizes a novel architecture, whichlearns at multiple resolutions while generating saliency maps with weaksupervision that are used to generate preliminary heat map visualizationdata.

As used herein, x∈

^(w×h×c) denotes an input image with width w, height h, and channel c.In particular, x can correspond to the medical scan received by themulti-label medical scan analysis system 3002. As used herein, y is abinary vector of dimensionality K, where K is the total number ofclasses. In particular, K can correspond to the size of the set ofabnormality classes discussed herein. For a specific class k, y_(k)=0indicates its absence and y_(k)=1 its presence. The subscript indexes aparticular example, for instance, {x_(i); y_(i)} is the i-th example. Asused herein, F∈

^(w×h×c) denotes a feature map and Q∈

R^(w×h×K) denotes a saliency map, with Q∈[0,1]. As used herein, twodepth factors l and m accompany the feature and saliency maps. Forinstance, F^(l) is the feature map as the result of a set of nonlineartransformation that changes the spatial resolution of F^(l−1). On theother hand, F_(m−1) and F_(m) are consecutive feature maps that preservethe resolution during the nonlinear transformation.

FIG. 13A illustrates an example model that can be utilized by themulti-label medical scan analysis system 3002. In particular, FIG. 13Aillustrates an example inference function 3020 that produces a saliencymap with a resolution of 64×64, illustrating the process from inputX-ray image to a predicted abnormality score. To reduce the resolution,a standard ResNet is firstly applied on the input image. To preserve theresolution, a standard DenseNet is applied per resolution. Upsamplingand channel-wise concatenation fuse information from multipleresolutions. LSE-LBA pooling aggregates instance scores to the globalprobability Different numbers of resolution layers and/or differentresolutions at each layer can be utilized in other embodiments. In someembodiments, the inference function 3020 and/or the medical labelingfunction 3030 can be implemented by utilizing the model of FIG. 13A asdiscussed herein.

Each ResNet can contain several sub-modules, each of which isparameterized as F^(l+1)=σ(g(F^(l))+f(F^(l))). F^(l+1) can be half theresolution of P, and/or can have twice the number of channels as F^(l).σ can be is an element-wise nonlinearity. The functions g and f can becomposed of a series of 1×1 and 3×3 convolutions. The reduction inspatial resolution can be achieved by using convolutions with a stridesize 2. A simple f and complex g can be chosen such that f is as closeas possible to a simple identity transformation, leaving theheavy-lifting non-linear transformations to g to learn the residual. Insome embodiments, spatial resolutions can be preserved withF_(m+1)=σ(g(F_(m))+F_(m)) in which case f is chosen to be the identityfunction.

ResNets are susceptible to over-parameterization, which becomes criticalwhen residual connections are used repeatedly on the horizontal data rowin FIG. 13A without changing the spatial resolution. In the scenariowhere F^(l+1)=σ(g(F^(l))+(F^(l))) is applied repeatedly, a model couldsimply learn to ignore the capacity in g, especially when σ is arectified linear unit (relu). This would effectively defeat the purposeof inner-resolution propagation where a model is encouraged tospecialize in making predictions under a selected resolution l. To solvethis issue, the non-identity transformation on F^(l) can be enforcedexplicitly, which can include removing the residual connections. Becausethe resulting model would lose the attraction of being easy to optimize,DenseNets can be utilized, where the resolution-preservingtransformation is formulated as F_(m)=σ(f(F₁⊕F₂⊕ . . . ⊕F_(m))), where ⊕denotes the channel-wise concatenation of feature maps and f denotes aseries of resolution-preserving nonlinear transformations. This equationfor F_(m) enforces the nonlinear transformation f on all previousfeature maps without the possibility of skipping using identity mappingwhile still maintaining the desirable property of being easy to optimizedue to the direct connections with all previous feature maps. Such adesign effectively encourages the participation of all previous featuremaps in propagation.

Fine-scale features, computed at high resolutions, capture detailedappearance information while coarse-scale features, computed from lowerresolution representations of the data, capture semantic information andcontext. In deep neural networks utilized by the multi-label medicalscan analysis system 3002, fine-scale features are learned in theearliest layers and coarse-scale features are learned in the subsequentlayers, where the spatial resolution of the data has been reduced byrepeated downsampling operations. Thus, the model learns to construct afeature hierarchy in a fine-to-coarse manner. While the coarse-scalefeatures at the top of typical classification neural networks aresuitable for image-level classification, spatial information required toprecisely localize abnormalities is likely to be lost. If the model isexpected to predict not only what abnormalities are present in the imagebut where they are, then the spatial information must be reintegrated.

The model illustrated in FIG. 13A performs this incrementally, in acoarse-to-fine manner, by repeatedly performing the operation F_(m)^(l)=f(

(F_(n) ^(l+1))⊕(F_(m−1) ^(l))), where F_(m) ^(l) denotes the m-thresolution-preserving feature map at resolution level l, where F_(n)^(l+1) denotes the n-th feature map from the lower resolution level l+1,where F_(m−1) ^(l) denotes previous feature map at resolution level l,where

denotes the upsampling operation, and where ⊕ denotes the channel-wiseconcatenation. The upsampling operation

, can be implemented in various ways including bilinear interpolation,nearest-neighbors interpolation, and/or learnable transposedconvolutions. In the example embodiment discussed here,nearest-neighbors can be used to implement

.

Log-Sum-Exp Pooling with Lower-bounded Adaptation can be utilized totake a saliency map S of a particular class k and produces a final scorep, and can be defined as follows:

$p = {{{LSE}\text{-}{{LBA}(S)}} = {\frac{1}{r_{0} + {\exp(\beta)}}\log\left\{ {\frac{1}{wh}{\sum\limits_{i = 1}^{w}\;{\sum\limits_{j = 1}^{h}\;{\exp\left\lbrack {\left( {r_{0} + {\exp(\beta)}} \right)S_{i,j}} \right\rbrack}}}} \right\}}}$

As used herein, S∈Q^(w×h×1) denotes a two-dimensional saliency map for aparticular class k to be pooled. S_(i,j) denotes the (i, j)-th elementof S. In other embodiments, a Noisy-OR (NOR) function, generalized-mean(GM) function, and/or Log-Sum-Exponent (LSE) function can be utilized inperforming the pooling function to generate the final score p. The finalscore p can correspond to the global probability determined for thecorresponding abnormality class k as discussed in conjunction with FIG.12B. For example, the medical labeling function 3030 can be implementedby utilizing the LSE-LBA function, and/or another pooling function.

In addition to maintaining the benefits of using a pooling functionwhich balances average and max pooling, the LBE-LSA pooling function isrobust to the issue of numerical underflow when S_(i,j) is very close tozero, compared with other pooling functions such as NOR and GM pooling,due to the removal of the exponential that directly acts on S_(i,j).LSE-LBA also preserves probabilities. By bounding the values in S to bein the range [0; 1], the resulting score will also be in the sameinterval. Since the LSE-LBA function is monotonically increasing inS_(i,j), it attains its maximum value when all S_(i,j)=1, and itsminimum value when all S_(i,j)=0. When S is a map of all O's,LSE-LBA(S)=0 and, and when S is a map of all l's LSE-LBA(S)=1. A sigmoidactivation function can be used on each S_(i,j) to maintain thisproperty. In addition to being numerically stable in computation, ourthe LSE-LBA function reparametrizes a hyperparameter r, used in LSEpooling, with r=r₀+exp(β) where r₀ is a positive constant and β alearnable parameter. r can be lower bounded by r₀, expressing thesharpness prior of the pooling function. A large r₀ can encourage thelearned saliency map to have less diffuse modes.

The model of FIG. 13A can be utilized in a weakly-supervised settingwhere pixel-wise labels are not available and only image-levelannotations are utilized. Given the multi-resolution fused feature mapat the highest level resolution F⁰∈

^(w×h×c), it is further divided into a grid of N×N, with N being thechosen resolution of the final saliency map. In some embodiments, N=w=h,resulting in F⁰∈

^(N×N×c). Each of the N² c-dimensional vectors represents an instanceI_(n)(x) in the bag F⁰, where n={1, . . . N²}. The K-class instanceprobability is P(I_(n)(x))=sigmoid(WI_(n)(x)), where W is a K by cparameter matrix that is shared among all N² instances. This leads tothe final probabilistic saliency map S∈Q^(N×N×K). Following the LBE-LSApooling function, P(x)=LSE-LBA(S(x)), where prediction P (x) is aK-dimensional vector and represents, according to theprobability-preserving property of LSE-LBA pooling the probability of xbelonging to K classes. Hence, a multi-class cross-entropy cost can bedirectly computed given y.

FIG. 13B illustrates example output saliency maps at various resolutionsfor an input chest x-ray. The model can produce all of the saliencymaps, corresponding to each of the resolution levels, or can produce thehighest level saliency map only. The model of FIG. 13A can also beutilized to generate probability matrix data and/or global probabilitydata for any other types of medical scans described herein.

The model of FIG. 13A can be applied to datasets of medical scans, suchas the NIH Chest X-ray dataset, which contains 112,120 frontal-viewchest X-rays taken from 30,805 patients, where 51,708 images contain atleast one of 14 labeled pathologies, in a PNG format with a standardizedspatial resolution of 1024×1024. The 14 labeled pathologies cancorrespond to the set of abnormality classes, and can include, forexample, atelectasis, effusion, mass, pneumonia, consolidation,emphysema, pleural thickening, cardiomegaly, infiltration, nodule,pneumothorax, edema, fibrosis, and/or hernia. Other clinical informationincluding patients' age and gender are accessible in addition to thepathology labels, and while not used in this example embodiment, can beutilized in other example embodiments to train the model and/orimplement the inference function.

For computational efficiency, the inputs of 1024×1024 can be downsampledto 512×512. Data augmentation can be applied during training, forexample, where each image is zoomed by a factor uniformly sampled from[0.25; 0.75], translated in four directions by a factor uniformlysampled from [−50; 50] pixels, and/or rotated by a factor uniformlysampled from [−25; 25] degrees. After data augmentation, the inputs canbe normalized to the interval [0; 1]. To further regularize the model, aweight decay can be applied, for example, with a coefficient of 10⁻⁵.

The model can be trained from scratch using only the NIH training set,for example, with an Adam optimizer and a learning rate of 0.001. Earlystopping can be performed on the validation set based on the average AUC(Area Under the ROC curve) over all of the set of pathologies. Forclassification, the AUC per abnormality can be utilized.

In some embodiments, no bounding boxes are used at training time so thatthe model remains weakly supervised with respect to the task oflocalization. The best models on the classification task can then beevaluated on their localization performance. The quality of localizationcan be determined using the metric of intersection over detectedbounding boxes (IoBB) with T(IoBB)=α, where α is set at a certainthreshold. IoBB can be extremely sensitive to the choice of thediscretization threshold by which the predicted probability score S isbinarized before being compared with ground truth bounding boxes. IoBBcan be very sensitive to the choice of a binarization threshold τ todiscretize probabilistic saliency maps into binary foreground andbackground masks.

In some embodiments, the continuous version of DICE=(2×S×G)/(S²+G²) canbe utilized as the cost function for training the model as a semanticsegmentation model, where S is the probabilistic saliency map directlyoutput by the model, and where G the ground truth binary bounding boxdownsampled to 512×512, the same resolution as the model input. The DICEcost function, or another cost function, can be selected to take intoaccount the probability while avoiding the decision of having to selectthe discretization threshold τ.

FIG. 13B presents a table illustrating an example of abnormalityclassification and weakly supervised localization performance on 14abnormalities on the NIH Chest X-ray test set. Three models withdifferent lower-bounded adaptation r₀ are included. In some embodiments,the impact of r₀ is much more pronounced in localization than inclassification. In this example, the model is only trained on NIH data.In other embodiments, a pre-trained model, for example, trained onImageNet without multi-resolution fusion. The bolded numbers of thetable of FIG. 13B indicate the maxima other than statisticalsignificance. Compared with classification, the choice of r₀ can have amore significant impact on abnormality localization due to their likelydistinct visual appearance. For instance, when r₀ is small and thesharpness prior is weak, a model can tend to perform well on visuallydiffused abnormalities such as cardiomegaly, infiltration and pneumonia.As the sharpness prior is strengthened, localization of focalized andpatchy abnormalities can be improved, as in the case of atelectasis andnodule. When choosing r₀ to be large, the performance of diffusedabnormalities can degrade, such as atelectasis, cardiomegaly, effusionand pneumonia.

FIG. 13C includes an example of model-generated saliency maps. Inparticular, for each of the abnormality classes cardiomegaly,infiltration, nodule, effusion, mass, and pneumonia, FIG. 13C includes,from left to right, original images, ground truth bounding boxes, andmodel generated saliency maps for each of r₀=0, r₀=5, r₀=10,respectively. The corresponding DICE score for each model-generatedsaliency map 3262, computed with respect to the ground truth, is alsopresented above the corresponding saliency map. FIG. 13C illustratesthat increasing r₀ can result in overall sharper saliency maps. Usingbounding boxes to delineate abnormalities can be limited byover-estimating their true ROIs, which is illustrated in the cases ofinfiltration and pneumonia. As illustrated in FIG. 13C, some modelfindings can be incorrectly marked as false positives due to labelingnoise wherein the ground-truth reader missed the finding.

FIG. 13D illustrates another example of saliency maps at multipleresolutions generated for a chest x-ray with mass by utilizing the fourmodels, with an increasing target resolution, that were trained, forexample, as discussed in conjunction with FIGS. 13A-13C, to produce thepresented visualization. In particular, FIG. 13D illustrates howmulti-resolution, lower-resolution maps can provide weak localizationcues that are refined in higher-resolution layers. In some embodiments,only a highest resolution saliency map, such as a 64×64 resolutionsaliency map, is generated for an input medical scan.

As previously discussed, the interface used by the client device 120 canfurther operate to respond to user interactions with the interface toselect the custom heat map settings. These custom heat map setting caninclude any of the various examples of post-processing methodologiesdiscussed in conjunction with the operation of post-processing function3060. In this fashion, for example, a radiologist/user determine and setwhich heat maps settings they prefer that are stored as custom heat mapsettings and used as a default for processing incoming scans. Factordata received via network 150 can also used by the interface of clientdevice 120 to suggest particular custom heat map settings for differentradiologists/users based on the cognitive factors and/or systematicfactors determined to be prevalent in errors for these differentradiologists/users.

FIG. 13D shows the preliminary heat map visualization data generatedbased on these saliency maps for a mass detected by the AI model in achest x-ray. Furthermore, a smoothed region boundary corresponding tothe detected mass is generated, based on factor data and/or custom heatmap settings, in a step of post-processing by the postprocessingfunction 3060 using border detection, a smoothing filter or other bordersmoothing function. In addition, a customized segmentation mask or othermasking function is applied, based on factor data and/or custom heat mapsettings, to eliminate artifacts in the preliminary heat mapvisualization data outside of the border.

FIGS. 13E-G illustrate example heat map visualization data in accordancewith various embodiments. In FIG. 13E, example heat map visualizationdata is presented where the detected region corresponding to thedetected mass is indicated, based on factor data and/or custom heat mapsettings, by a solid region of constant color and intensity. In FIG.13F, example heat map visualization data is presented where a gradientblur is applied, based on factor data and/or custom heat map settings,to the detected region corresponding to the detected mass. This yields aregion of constant color but an intensity that varies in brightness frommost bright in the centroid of the region tapering off to least brightat the boundaries of the region. In FIG. 13G, example heat mapvisualization data is presented where, based on factor data and/orcustom heat map settings, border smoothing is not applied. Further,based on custom heat map settings, only patches having probabilitiesabove a selected threshold value are highlighted. The pixelization ofthe detected region based on the 64×64 saliency map is maintained inthis case. In this case, the use of the selected threshold value servesto eliminate artifacts in the preliminary heat map visualization dataoutside of the detected region—without the need of a separate maskingfunction.

FIG. 14A illustrates a multi-label heat map display system 3100 thatinteracts with an associated display device 3170 to display heat mapvisualization data and/or a set of heat maps via an interactiveinterface 3110. The heat map visualization data can be generated by andreceived from the multi-label medical scan analysis system 3002 asdescribed in conjunction with FIGS. 12C, 13A-13G and/or the heat mapvisualization data can be retrieved from another subsystem 101. Forexample, the client device 120 can be implemented as the multi-labelheat map display system 3100, where the multi-label heat map displaysystem is an application stored in memory of the client device and runby the processing system of the client device to display the interactiveinterface 3110, for example, by utilizing the display device 270 of FIG.2A to generate the interactive interface 275. In some embodiments, themulti-label medical scan analysis system 3002 is utilized to implementthe multi-label heat map display system 3100 by utilizing its owndisplay device 3170. In some embodiments, the multi-label heat mapdisplay system 3100 is an addition subsystem 101.

In various embodiments, a multi-label heat map display system 3100 isoperable to receive, via a receiver, a medical scan and heat mapvisualization data corresponding to a set of heat maps of the medicalscan, where each of the set of heat maps corresponds to post-processedprobability matrix data generated for a corresponding one of a set ofabnormality classes by utilizing the medical scan as input to aninference function. An interactive interface is generated for display ona display device associated with the multi-label heat map displaysystem. A first portion of the interactive interface displays image dataof the medical scan, and a second portion of the interactive interfacedisplays at least one of the set of heat maps. The first portion of theinteractive interface can be adjacent to the second portion of theinteractive interface. User input to a client device associated with themulti-label heat map display system is received, where the user inputcorresponds to a selection by a user from option data presented by athird portion of the interactive interface. An updated interactiveinterface is generated for display on the display device, where theupdated interactive interface includes a change to the display of the atleast one of the set of heat maps by the second portion of theinteractive interface in response to the user input.

FIGS. 14B and 14C present example views presented by interactiveinterface 3110. The position and orientation of different elements ofthe interface 3110 can be the same or different than those presented in14B and 14C, and the features discussed in conjunction with FIGS. 14Band 13C can be utilized in different arrangements of some or all of theelements displayed by the interface 3110. In particular, the heat mapselection interface 3135 can be displayed in its own window in anyportion of the interface, and can dynamically display different optionsas the user makes selections of heat maps for display based on customheat map settings and/or and further permits selection/adjustment of thecustom heat map settings corresponding to each of the K abnormalitiesclasses. In some embodiments, the heat map selection interface 3135 isnot displayed as its own window, and selections made by the user cancorrespond to direct interaction with one or more heat maps displayed bythe interface, interaction with the original medical scan displayed bythe interface, or other interaction with the interface 3110 indicatingthe selections from presented options or other input utilized by theinterface 3110 as discussed herein. Any embodiments of the heat mapvisualization data described in conjunction with FIG. 12C can bedisplayed by the interactive interface 3110. Any of the embodiments ofthe heat map visualization data described in conjunction with FIG. 12Ccan correspond to options presented to the user via the interface,custom heat map settings, factor data and/or can correspond to selectiondata received as user input.

FIG. 14B illustrates an example of a view of interactive interface 3110that can be displayed by the display device of client device 120, inresponse to receiving heat map visualization data. Interface 3110 candisplay some or all of the heat maps, for example, side by side inadjacent views, and/or otherwise simultaneously. In some embodiments,some or all heat maps are displayed in a table 3120 as shown in FIG.14B. While table 3120 is depicted as a single row, with K columns, table3120 can include a single column with K rows, and/or can include anynumber of rows and columns in other embodiments. In some embodiments,the interface also displays the original medical scan 3112 alongside thesimultaneous display of the one or more of the heat maps. The originalmedical scan 3112 can be displayed statically and/or unaltered. Theoriginal medical scan 3112 can be displayed at the same size as eachheat map in table 3120 for ease of comparison. Furthermore, the originalmedical scan 3112 can be aligned along a same horizontal axis or a samevertical axis as a row or column of the table 3120 for further ease ofcomparison. In some embodiments, the region of interest data generatedby the multi-label medical scan analysis system 3002 can be received andpresented in conjunction with display of the heat map and/or theoriginal medical scan 3112. In some embodiments, other annotation dataand/or diagnosis data generated in the inference data, for example,based on the detection step 1372 and/or the classification step 1374,can be presented in conjunction with display of the heat map and/or theoriginal medical scan 3112.

A heat map selection interface 3135 can be utilized to allow a user toselect and/or toggle custom heat map settings used for generation anddisplay of the table of heat maps. The user can correspond to aradiologist, a user responsible for generating and/or maintaining thecomputer vision model, a system administrator, and/or another user ofone or more subsystems 101. For example, the user can select from aplurality of heat map ordering criteria options, where the order of theK heat maps is determined based on the ordering criteria identified bythe user to order the K heat maps. For example, the user can select toorder the heat maps in descending order by their corresponding globalprobabilities. As another example, the user can select from a pluralityof proper subset criteria options, where only a proper subset of the Kheat maps that meet the selected one or more proper subset criteriaoptions are displayed. For example, the user can select to display onlyheat maps with a corresponding global probability for the correspondingabnormality class that compares favorably to a probability threshold. Insome embodiments, the user can select from a set of probabilitythresholds and/or can enter the probability threshold as a continuousvalue. In embodiments where heat maps are generated for multipleresolution layers, all of the resolution layers can be displayed by thetable and/or each of the K heat maps are displayed at a selectedresolution layer selected by the user utilizing the heat map selectioninterface 3135.

FIG. 14C illustrates another example of a view of interactive interface3110 that can be displayed by the display device of client device 120.In some embodiments, a window 3130 of the interface is designated fordisplay of a single one of the K heat maps, denoted as heat map X inFIG. 14C. In some embodiments, heat map X is selected in response touser selection of one of the K abnormality classes and/or one of the Kheat maps themselves. For example, heat map selection interface 3135 candisplay a list of the K abnormality classes and/or can display the table3120, and the user can interact with the heat map selection interface3135 to indicate which heat map is shown. As another example, theinterface 3110 can change to the new view of FIG. 14C displaying window3130 in response to selection of the one of the K heat maps from table3120 by user interaction to the view of interface 3110 presented in FIG.14B. For example, the table 3120 can correspond to small, thumbnailimages of the K heat maps that, when selected, cause the window 3130 todisplay the selected heat map as a larger display. In some embodimentsthe heat map X is further selected based on selection of the one of theplurality of resolution layers. In some embodiments, the user can togglebetween heat maps displayed in the window by changing their selection ofthe one of the K abnormality classes and/or resolution layers byinteracting with heat map selection interface 3135. While FIG. 14Cpresents a single window, multiple windows can be included and can eachdisplay different heat maps.

In some embodiments, the interface 3110 also displays the image data ofthe original medical scan 3112, and/or the image data of the medicalscan as it was received by the multi-label heat map display system 3100,alongside the window 3130. The original medical scan 3112 can bedisplayed statically and/or unaltered. The original medical scan 3112can be displayed at the same size as the heat map X in window 3130 forease of comparison. Furthermore, the original medical scan 3112 can bealigned along a same horizontal axis or a same vertical axis as the heatmap X in window 3130 for further ease of comparison. In someembodiments, the region of interest data generated by the multi-labelmedical scan analysis system 3002 can be received and can be presentedin conjunction with display of the heat map and/or the original medicalscan 3112. In some embodiments, other annotation data and/or diagnosisdata generated in the inference data, for example, based on thedetection step 1372 and/or the classification step 1374, can bepresented in conjunction with display of the heat map and/or theoriginal medical scan 3112.

In some embodiments of interface 3110 as presented in FIG. 14B, FIG.14C, or another configuration of interface 3110, the user can interactwith the heat map selection interface 3135 or can otherwise interactwith interface 3110 to toggle the smoothing function, for example, wherethe user can turn smoothing on or off and/or can alter the smoothingparameters of the smoothing function. As a result, the interface candisplay one or more heat maps in accordance with the selected parametersand/or without any smoothing in response to the user input. In someembodiments, the multi-label heat map display system 3100 receivessmoothing function parameters and/or unsmoothing function parametersthat can be applied to smooth an unsmoothed heat map received in theheat map visualization data and/or to unsmooth a smoothed heat mapreceived in the heat map visualization data, respectively. For example,unsmoothing a smoothed heat map can result in the regenerating orotherwise reverting back to the original heat map before the smoothingfunction was applied. In other embodiments, the smoothing function is anon-reversible function, and the multi-label heat map display system3100 can receive a smoothed version and original version of each of theheat maps to enable the user to toggle between the smoothed andunsmoothed versions. In some embodiments, the multi-label heat mapdisplay system 3100 can store a copy of the original unsmoothed versionlocally after applying the smoothing function.

Alternatively or in addition, the user can interact with the heat mapselection interface 3135 or can otherwise interact with interface 3110to toggle the segmentation masking, for example, where the user can maskor unmask the region outside the anatomical region of interest in thedisplay of one or more heat maps. In some embodiments the user cantoggle between a plurality of anatomical regions by interacting with theinterface 3110, and as a result, the heat map will be displayed with thepixels outside the selected anatomical region masked. In someembodiments, the multi-label heat map display system 3100 receivesmasking function parameters and/or unmasking function parameters thatcan be applied to mask an unmasked heat map received in the heat mapvisualization data and/or to unmask a masked heat map received in theheat map visualization data, respectively. For example, unmasking amasked heat map can include regenerating or otherwise reverting back tothe original heat map before the masking function was applied. In otherembodiments, the masking function is a non-reversible function, and themulti-label heat map display system 3100 can receive an unmasked versionand a masked version, or a plurality of masked versions corresponding toa plurality of different anatomical regions, of each of the heat maps toenable the user to toggle between the masked and unmasked versions. Insome embodiments, the multi-label heat map display system 3100 can storea copy of the original unmasked version locally after applying themasking function. The masking and smoothing can be toggled separately orsimultaneously, for example, where a heat map is displayed as bothmasked and smoothed.

In some embodiments, for example, where each image patch corresponds tothe same color value, each image patch and their correspondingprobabilities are visually distinct. For example a heat map where nosmoothing function was applied and/or a heat map that was unsmoothed andreverted back to its original form can have visually distinct imagepatches. In some embodiments, the user can interact with window 3130 ofFIG. 14C to select a particular image patch of interest. In response,the interface 3110 can outline, highlight, crop, zoom in on, orotherwise indicate the corresponding image patch in the original medicalscan 3112. This can enable the user to more easily inspect the featuresof the image that resulted in the corresponding probability matrix valueof the image patch, for example, allowing the user to detect features inthe original image that were improperly detected by the model as one ofthe K abnormalities and/or allowing the user to detect features in theoriginal image that should correspond to one of the K abnormalities, butwere overlooked by the model.

The user can interact with the interface 3110 or otherwise interact withthe client device to correct heat maps, global probabilities and/orfinal binary identifiers. For example, the multi-label heat map displaysystem 3100 can generate error data based on user input corresponding toerrors identified by the user. In some embodiments, the user canidentify errors in one or more image patches of a heat map by selectingthe one or more image patches. The user can identify whether thecorresponding probabilities of the one or more image patches were toohigh or too low. Correction data can be generated based on user overrideof the probability of the one or more image patches, for example, wherethe user indicates a binary identifier indicating whether thecorresponding abnormality is present or absent, and the probabilityvalue can be replaced by the binary identifier. In some embodiments, anew, corrected probability matrix and/or heat map can be generatedand/or displayed by the interface based on the user input and/or can betransmitted to another client device, and/or for transmission back tothe multi-label medical scan analysis system 3002, for example to assistin retraining of the model. In some embodiments, the user can correctfinal binary indicators for some or all of the K abnormality classes,for transmission to the medical scan database, for transmission toanother client device, and/or for transmission back to the multi-labelmedical scan analysis system 3002, for example to assist in retrainingof the model.

Alternatively or in addition, the user can initiate remediation of themodel via interaction with the interface 3110 or otherwise interactingwith the client device, for example, based on their review of heat mapsfor multiple medical scans. In response, the client device can transmitremediation instructions to the multi-label medical scan analysis system3002 and/or another subsystem 101, and remediation can be performed inresponse, for example, by performing remediation step 1140. In someembodiments, the remediation instructions can include updated modelparameters and/or can indicate error data and/or correction dataindicating errors identified in and/or corrections made to probabilitiesof one or more image patches of one or more heat maps, the probabilitymatrix, heat map, global probabilities, and/or final binary identifiersfor use in retraining the model and/or other remediation.

In some embodiments, the heat map visualization data indicates apredetermined ordering for the K heat maps. For example, the K heat mapscan be sorted in descending order of the calculated global probabilityof the corresponding abnormality class, where the heat map of the one ofthe K heat maps with the highest corresponding global probability valueis first in the predetermined ordering. As another example, thepredetermined ordering corresponds to a determined severity ortime-sensitivity of the corresponding abnormality class, which can bedetermined based on features of the abnormality detected in the medicalscan. As another example, the predetermined ordering is selected by theuser via the interface and/or is stored as user preference datacorresponding to the user. In some embodiments, the predeterminedordering corresponds to a proper subset of the K classes, for example,where only heat maps for abnormality classes determined to be present inthe medical scan are included in the predetermined ordering. In someembodiments, the user can override the predetermined ordering via userinput to the interface.

The table 3120 can be automatically arranged based on the predeterminedordering determined by custom heat map settings and/or factor data, forexample, where the first heat map of the predetermined ordering appearsat the top of the table and where the last heat map of the predeterminedordering appears at the bottom of the table. As another example, thefirst heat map of the predetermined ordering can appear at the top-leftmost spot of the table and the last heat map of the predeterminedordering appears at the bottom-right most of the table. In embodimentswhere a single heat map is displayed by window 3130, window 3130 candisplay the heat maps in accordance with the predetermined ordering, oneat a time, in sequence, where the window displays a next heat map in thepredetermined ordering in response to user input indicating the userelects to advance to the next heat map.

In some embodiments, interface 3110 can present multiple heat mapsoverlapping each other in the same window 3130. For example, a singleset of pixels corresponding to the size of the medical scan can presentmultiple heat maps simultaneously. In particular, the multiple heat mapscan be presented in accordance with different color schemes, differentshading patterns, different animation patterns, or can otherwise bevisually distinguishable. In such embodiments, the two or more heat mapssimultaneously displayed in window 3130 can correspond to the first twoor more heat maps in the predetermined ordering and/or can be selectedbased on user input.

FIG. 15A is a schematic block diagram of a retroactive discrepancyflagging system in accordance with various embodiments. The retroactivediscrepancy flagging system 3300 can be utilized to flag medical scansbased on the result of performing an automated, retroactive review of aset of selected medical scans. Retroactive discrepancy notifications canbe generated that provide retrospective insights regarding potentialerrors made by medical professionals in reviewing a medical scan and/orgenerating a medical report. The retroactive discrepancy flagging system3300 improves the technology of viewing tools and review systems, byautomatically determining and flagging potential errors for furtheranalysis.

In various embodiments, sets of one or more medical scans, eachcorresponding to one of a set of patients and/or one of a set ofstudies, can be selected for retroactive review. These selected medicalscans and their corresponding medical reports can be retrieved inresponse. Automated assessment data can be generated for each of the oneor more medical scans by performing an inference function on the medicalscan by utilizing a computer vision model trained on a plurality ofmedical scans. Human assessment data, corresponding to diagnosis orfindings made by a radiologist or other human in conjunction withviewing the one or more medical scans, can be generated based onfindings extracted from the corresponding medical report. A consensusfunction can be performed by comparing the automated assessment data andthe human assessment data, and the retroactive discrepancy flaggingsystem can determine whether the comparison is favorable or unfavorable.When the result of the consensus function indicates that the comparisonis unfavorable, the corresponding one or more medical scans can beflagged in retroactive discrepancy notifications that can be transmittedto a client device for display and used for further analysis, forexample, to determine error factors and other trends associated withparticular medical professionals, institutions, viewing tools andspecific interface functions, medical conditions, in a fully automatedfashion.

As shown in FIG. 15A, the retroactive discrepancy flagging system 3300can communicate bi-directionally, via network 150, with the medical scandatabase 342, with a medical report database 392, with user database344, and/or with other databases of the database storage system 140,with one or more client devices 120, and/or, while not shown in FIG.13A, with one or more subsystems 101 of FIG. 1. In some embodiments, themedical report database can be implemented by utilizing report database2625. In some embodiments, medical reports are instead retrieved asreport data 449 from the medical scan database 342, and/or the medicalreport database 392 contains entries corresponding to report data 449 ofcorresponding medical scan entries of the medical scan database 342.

In various embodiments, the retroactive discrepancy flagging system 3300is implemented via at least one processor; and a memory that storesoperational instructions that, when executed by the at least oneprocessor, cause the retroactive discrepancy flagging system to receive,via a network interface such as subsystem network interface 265, amedical scan and a medical report corresponding to the medical scan thatwas written by a medical professional/user in conjunction with review ofthe medical scan. The retroactive discrepancy flagging system 3300 alsooperates to generate automated assessment data by performing aninference function 3310 on the first medical scan utilizing a computervision model trained on a plurality of medical scans; generates humanassessment data by performing an extraction function 3320 on the medicalreport; and further generates consensus data by performing a consensusfunction 3395 on the automated assessment data and the human assessmentdata. Performing the consensus function 3395 can include comparing theautomated assessment data to the human assessment data. The retroactivediscrepancy flagging system 3300 also operates to determine if theconsensus data indicates the automated assessment data comparesfavorably or unfavorably to the first human assessment data, i.e. todetermine if they match or they do not match. A retroactive discrepancynotification is generated in response to determining that the consensusdata indicates the automated assessment data compares unfavorably to thehuman assessment data.

In various embodiments, the retroactive discrepancy notificationincludes at least one image associated with the medical scan andretroactive discrepancy data that indicates at least one discrepancybetween the automated assessment data and the human assessment data. Forexample, the retroactive discrepancy notification can include anidentification of the medical scan, an identification of a particularsubset of images and/or image portions in the medical scan that includethe discrepancy and/or a plurality of medical conditions that aredetermined to be either present of absent, based on either the automatedassessment data or the human assessment data. The retroactivediscrepancy notification can also include or indicate the medical reportand an identification of the medical professional that generated themedical report, as well as information pertaining to the nature of thediscrepancy. For example, the retroactive discrepancy notification canindicate that the automated assessment data indicated the presence of aparticular abnormality or other medical condition while the humanassessment data did not or vice versa. The particular, an abnormality orother medical condition can be identified, for example, by including acorresponding medical code, medical term and or other abnormalityclassification data in the retroactive discrepancy notification. Inaddition or in the alternative, the retroactive discrepancy notificationcan provide other information regarding the generation of the medicalreport such as the time of day the report was generated, the number ofmedical reports generated by the user in a review session that includedthe subject medical report, the progress through the review session atthe time the report was generated, a preliminary diagnosis and/or arequest for review by the user by another medical professional, the typeof PACS viewer or other user interface that was used by the user togenerate the report, and/or other data or metadata derived from themedical report or medical scan.

The retroactive discrepancy flagging system 3300 also operates totransmit, via the network interface such as subsystem network interface265, the retroactive discrepancy notification via the network 150 toother subsystems 101. In various embodiments, the retroactivediscrepancy flagging system 3300 is itself an additional subsystem 101of the medical scan processing system 100, implemented by utilizing thesubsystem memory device 245, subsystem processing device 235, and/orsubsystem network interface 265 of FIG. 2B. In some embodiments, theretroactive discrepancy flagging system 3300 utilizes, or otherwisecommunicates with, the central server system 2640. For example, some orall of the databases of the database storage system 140 are populatedwith de-identified data generated by the medical picture archiveintegration system 2600. The retroactive discrepancy flagging system3300 can receive de-identified medical scans, annotation data, and/orreports directly from the medical picture archive integration system2600. For example, the retroactive discrepancy flagging system 3300 canrequest de-identified medical scans, annotation data, and/or reportsthat match requested criteria. In some embodiments, some or all of theretroactive discrepancy flagging system 3300 is implemented by utilizingother subsystems 101 and/or is operable to perform functions or otheroperations described in conjunction with one or more other subsystems101.

The retroactive discrepancy flagging system 3300 can retroactivelyselect one or more medical scans for review. The one or more medicalscans can be selected randomly, pseudo-randomly, as part of anon-random/systematic audit, can be selected based on selected criteria,can be selected based on a peer-review schedule, can be selected basedon a determined proportion of medical scans to review, can be determinedbased on a selected frequency or rate of medical scans to review withina time frame. Further, such an audit can be a non-random auditassociated with the particular medical professional triggered by theidentification of one or more prior errors associated with one or moreprior retrospective discrepancy notifications or otherwise based on anumber of medical scans, such as more than a threshold number, that havepreviously been flagged for review, can be otherwise selected based onprior review results, can be selected in response to identifyingrepeated or systematic or cognitive errors associated with a particularPACS viewing system, user, and/or institution, can be selected based onthe presence or absence of a particular medical condition and/or can beselected based on other factors. This selection can include selectingthe number of medical scans for review; selecting medical scans forreview that correspond to a selected medical scan type, modality and/ora selected anatomical region; selecting medical scans for review where aselected medical professional authored or otherwise generated thecorresponding annotation data, diagnosis data, and/or report data;selecting medical scans for review associated with a selected medicalinstitution; selecting medical scans for review associated with aselected geographic region; selected selecting medical scans for reviewassociated with a selected diagnosis type; selecting medical scans forreview associated with patients that meet selected patient history orpatient demographic criteria; selected selecting medical scans forreview based on other selection criteria and/or otherwise selectingmedical scans based on received criteria and/or criteria automaticallydetermined by the retroactive discrepancy flagging system 3300. Some orall of the selection criteria can be received via user input to a userinterface, via the network, and/or via one or more other subsystems 101.

The selection criteria and/or identifiers for selected medical scans,medical reports, medical professionals, medical institutions, and/orpatients can be utilized to by the retroactive discrepancy flaggingsystem 3300 to fetch the selected medical scans and/or correspondingmedical reports from database system 140. In various embodiments, themedical scans and/or corresponding medical reports can be retrieved froma medical picture archive system and/or a report database. In someembodiments, the medical scans and/or corresponding medical reports canbe de-identified prior to review, for example, by utilizing the medicalpicture archive integration system 2600.

Upon receiving the medical scan and/or the medical report, humanassessment data can be generated by applying an extraction function 3320to the medical report. In some embodiments, only medical scans arereceived, and extraction function 3320 is applied to metadata of themedical scan or other human-generated findings included along with imagedata of the medical scan. The human assessment data can correspond tothe human assessment data discussed in conjunction with the lesiontracking system 3002, can correspond to annotation data generated by amedical professional, can correspond to measurements made by a medicalprofessional of lesions or other abnormalities detected by the medicalprofessional, can correspond to a classification made by the medicalprofessional of one or more abnormalities or other medical conditionsdetected by the medical professional, can correspond to a diagnosis madeby the medical professional, and/or can correspond to other measurementsor findings in the medical scan, determined by a human. The extractionfunction can be utilized to extract the human assessment data frommetadata of the medical scan, from fields of the medical scan entry 352such as from diagnosis data 440, and/or from the text, metadata, orother fields of the medical report. In some embodiments, performing theextraction function 3320 can include performing a medical scan naturallanguage analysis function and/or inference function. For example, themedical scan natural language analysis function can be performed on textcorresponding to the findings made by the medical professional, such astext of the medical report.

Automated assessment data is generated by performing at least oneinference function 3310 on the medical scan. In some embodiments, theinference function 3310 can be performed on the image data of medicalscans alone. In other embodiments, the inference function 3310 canutilize other pertinent data in addition to the image data, such aspatient history or other data of the medical scan entry 342, to generatethe automated assessment data. The inference function 3310 can utilize acomputer vision model, for example, trained by medical scan imageanalysis system 112 on a plurality of medical scans as discussed herein.Performing the inference function 3310 can include performing one ormore lesion measurement functions discussed herein to generatemeasurement data, and/or the automated assessment data can correspond tothe automated assessment data discussed in conjunction with a lesiontracking system. Performing the inference function 3310 can includeperforming any medical scan analysis function and/or inference functiondiscussed herein to generate automated assessment data that correspondsto automatically generated annotation data, diagnosis data, abnormalitydetection data, and/or abnormality classification data associated withan abnormality or other medical condition.

Consensus function 3395 can be performed on the human assessment dataand the automated assessment data to generate consensus data. One ormore types of fields and/or values of the automated assessment data cancorrespond to one or more same types of fields and/or values of thehuman assessment data to enable comparison of the human assessment dataand the automated assessment data. Performing the consensus function caninclude measuring a disagreement between the automated assessment dataand the human assessment data and determining whether the measureddisagreement compares favorably or unfavorably to a disagreementthreshold. For example, measuring the disagreement can includeperforming a similarity function, can include computing a difference invalues or logical results (e.g. yes versus no, a condition is presentversus a condition is not present, a scan is normal versus a scan isabnormal, etc.), can include determining whether or not the values orlogical results match or otherwise compare favorably, and/or bycomputing a Euclidean distance between feature vectors of the humanassessment data and the automated assessment data. When the measureddisagreement compares unfavorably to the disagreement threshold, thecomparison is determined to be unfavorable, and when the measureddisagreement compares favorably to the disagreement threshold, thecomparison is determined to be favorable. The disagreement threshold canbe set or predetermined to permit no level of disagreement or can be setor predetermined to permit some modest level of disagreement. Medicalscans yielding an unfavorable comparison in performing the consensusfunction can be flagged for generation of a corresponding retroactivediscrepancy notification.

In various embodiments, retroactive review/audit of entire studies isconducted. The retroactive discrepancy flagging system can retrieve aplurality of sets of longitudinal data for review, where each set oflongitudinal data corresponds to one of a set of patients. Theretroactive discrepancy flagging system can extract the human assessmentdata for each set of longitudinal data, can generate automatedassessment data for each set of longitudinal data, and can compare thishuman assessment data to the automated assessment data for each set oflongitudinal data. The longitudinal data corresponding to a patient canbe is reviewed, for example, as discussed in conjunction with a lesiontracking system. The human assessment data can correspond to humanmeasurement of one or more lesions and/or human classification of one ormore lesions. The automated assessment data can be generated byperforming the one or more lesion measurement functions and/or byclassifying the lesion by performing abnormality classification step1374 as discussed herein. The consensus function can be performed, andwhen the human assessment data and the automated assessment data of aset of longitudinal data yield an unfavorable comparison, the entirestudy can be flagged for generation of a corresponding retroactivediscrepancy notification.

FIG. 15B is a schematic block diagram of a factor detection system inaccordance with various embodiments. In particular, a factor detectionsystem 4300 is presented in a system that includes several similarelements presented in FIGS. 15A and 12C that are referred to by commonreference numerals. In various embodiments, the factor detection system4300 is further configured to transmit the factor data to the medicalscan analysis system 3002 for use in selecting/adjusting custom heat mapsettings, for example, relating to particular post processing techniquesto be employed in generating heat map visualization data. Furthermore,the factor data can be sent to the client device 120 implementing amulti-label heat map display system 3100 and display device 3170, a PACSviewing system or other medical scan viewing and annotation tool (or“viewing tool”) used in generating medical reports. In response, thecustom heat map settings are selected/adjusted based on the cognitivefactors and/or systematic factors identified for the user/radiologist inthe factor data, and used in generating the heat map visualizationfunctions and features presented to the radiologist during their reviewof a scan.

For example, based on the cognitive factors and/or systematic factorsidentified in the factor data for a particular radiologist, thatradiologist can presented with customized heat maps during theirsubsequent reviews of scans, with the goal of preventing future errorsand lowering the radiologists error rate. This improves the technologyof viewing tools by assisting, in a fully automated way, the review ofsuch scans and helping to prevent future errors. In various embodiments,the factor data indicates one or more conditions and customized heat mapsettings are selected/adjusted in response to an occurrence of one ormore conditions.

For example, if a radiologist makes frequent errors after 5 pm, thecustom heat map settings can be adjusted to override a preselection of asubset of K abnormalities and force the user to review all of the Kabnormality classes. In some cases, heat maps can be presenteddifferently based on systematic/cognitive factors. For example, aradiologist may be presented with the scan and corresponding heat mapsfirst and may be forced to begin annotating the scan before beingallowed to view the report/referral/symptoms sent by a clinician (if theradiologist has tendencies of anchoring bias). As another example,interface features of the viewing tool may be adjusted to present heatmaps differently based on systematic factors indicating the radiologiststruggles to use particular tools correctly. As another example, theprobability threshold values and/or segmentation/masking functionsand/or other custom heat map settings used to generate the heat mapvisualization data, can be set differently for different radiologistsbased on their cognitive factors (e.g. more sensitive to show morepossible abnormalities if the radiologist tends to have satisfaction ofsearch cognitive factors) and/or at different times of day (i.e. moresensitive after 5 pm to make sure the radiologist checks everything ifthey tend to miss abnormalities after 5 pm).

In various embodiments, the factor detection system 4300 is configuredto generate factor data, based on a plurality of retroactive discrepancynotifications from the retroactive discrepancy flagging system 3300. Thefactor data identifies one or more factors, that contribute to errorsassociated with the either a particular medical professional, a selectedsubset of medical professionals, an entire set of medical professionals,a PACS viewer or other viewing tool or interface feature thereof, amedical institution, or other entity. For example, factor detectionsystem 4300 can analyze the retroactive discrepancy notifications overtime to evaluate and identify trends in the mistakes in reviewingmedical scans made by radiologists or other medical professionals andfurther to identify systematic factors, cognitive factors and/or otherfactors that appear to lead these errors. This improves the technologyof viewing and analysis tools and systems by assisting, in a fullyautomated way, the identification of errors and bias that can be used tohelp to prevent future errors.

In various embodiments, the retrospective discrepancy flagging system3300 can identify a set of medical scans reviewed by a particularradiologist and corresponding medical reports, including the scans thatwere identified as having a discrepancy with either model generatedoutput or an audit generated by other reviewers. A non-random audit canbe automatically performed by the retrospective discrepancy flaggingsystem 3300 on scans/reports that utilizes model output to determinescans with discrepancies based on, for example, retrospectivediscrepancy notification, and/or a random audit can be performedautomatically to gather scans for a particular radiologist and determinetheir error rate based on their scans with discrepancies detected in thenon-random audit. For example, radiologists can be flagged for thisprocess only if their error rate (determined from random audit) exceedsa threshold. The set of medical scans/reports can be selected for auditrandomly, pseudo-randomly, as part of a non-random/systematic audit, canbe selected based on selected criteria, can be selected based on apeer-review schedule, can be selected based on a determined proportionof medical scans to review, can be determined based on a selectedfrequency or rate of medical scans to review within a time frame.Further, such an audit can be a non-random audit associated with theparticular medical professional triggered by the identification of oneor more prior errors associated with one or more prior retrospectivediscrepancy notifications or otherwise based on a number of medicalscans, such as more than a threshold number, that have previously beenflagged for review, can be otherwise selected based on prior reviewresults, can be selected in response to identifying repeated orsystematic or cognitive errors associated with a particular PACS viewingsystem, user, and/or institution, can be selected based on the presenceor absence of a particular medical condition and/or can be selectedbased on other factors. This selection can include selecting the numberof medical scans for review; selecting medical scans for review thatcorrespond to a selected medical scan type, modality and/or a selectedanatomical region; selecting medical scans for review where a selectedmedical professional authored or otherwise generated the correspondingannotation data, diagnosis data, and/or report data; selecting medicalscans for review associated with a selected medical institution;selecting medical scans for review associated with a selected geographicregion; selected selecting medical scans for review associated with aselected diagnosis type; selecting medical scans for review associatedwith patients that meet selected patient history or patient demographiccriteria; selected selecting medical scans for review based on otherselection criteria and/or otherwise selecting medical scans based onreceived criteria and/or criteria automatically determined by theretroactive discrepancy flagging system 3300. The factor detectionsystem 4300 can evaluate trends identified this set of medical scans(for example, based on information presented in the metadata of eachscan/report or extracted from output of a model performed on eachscan/report and further based on retrospective discrepanciesnotifications corresponding to some subset of the total medicalscans/reports in the audit) to determine particular systematic factorsand/or cognitive factors that motivate the errors.

Identifying systematic and cognitive factors can include considering thereport/referral sent to the radiologist to the radiologist's review,determining a proportion of all errors made by a radiologist that meetthe trend criteria and comparing to a threshold, and/or determining ifthis trend criteria was present in less than a proportion of allcorrectly reviewed scans and comparing this proportion to athreshold—e.g, a trend of “the radiologist makes most of his errorsafter 5 pm” is not valid as a systematic error if the radiologistreviews every scan after 5 pm. Identifying a factor could includecomparing trends detected across all radiologists in this process, anddetecting common trends correlated to errors and/or non-errors acrossall radiologists (e.g. scans with errors across many radiologistscorrespond to a significant proportion of the scans reviewed by theseradiologists after 5 pm—this could imply causation or otherwise indicatethat post 5 pm review is a common trend across many radiologists thatcan be directly searched for in the future). Identifying a factor caninclude linking the factor to a particular type of scan/bodypart/abnormality (e.g. framing bias is predominant for radiologist Xwhen presented with scans that include cardiomegaly).

Identified factors can be presented to hospitals/medical entities forreview of their radiologists, to PACS tool makers or interfacedevelopers (if there are predominant trends in a particular tool causingsystematic errors), and/or to radiologists to guide improvement inreviews. Identified factors can be processed and analyzed over allradiologists/radiologists at particular hospitals/radiologists inparticular geographic regions/radiologists in particular fields ofstudy/etc. to determine if there are predominant systematic factorsand/or cognitive factors across the same hospital/geographicregion/field of study/etc.

In some embodiments, the factor detection system 4300 is an additionalsubsystem 101 of the medical scan processing system 100, is configuredto bidirectionally communicate with the network 150 and further operatesby utilizing the subsystem memory device 245, subsystem processingdevice 235, and/or subsystem network interface 265 of FIG. 2B. Whileshown separately from the retroactive discrepancy flagging system 3300,in other examples, these two systems can be implemented together in asingle platform with at least one processor and a memory that storesoperational instructions to perform both functions.

As previously discussed, the retroactive discrepancy notifications caninclude an identification of a medical scan, an identification of aparticular subset of images and/or image portions in the medical scanthat include the discrepancy. The retroactive discrepancy notificationcan also include or indicate the medical report and an identificationthe medical professional that generated the medical report, as well asinformation pertaining the nature of the discrepancy. For example, theretroactive discrepancy notification can indicate that the automatedassessment data indicated the presence of a particular abnormality whilethe human assessment data did not or vice versa. The particular anomalyor other condition can be identified, for example, by including acorresponding medical code, medical condition term and or otherabnormality classification data in the retroactive discrepancynotification. In addition or in the alternative, the retroactivediscrepancy notification can provide other information regarding thegeneration of the medical report such as the time of day the report wasgenerated, the number of medical reports generated by the user in areview session that included the subject medical report, the progressthrough the review session at the time the report was generated, apreliminary diagnosis and/or request for review by the user by anothermedical professional, the type of PACS viewer or other user interfacethat was used by the user to generate the report, and/or other data ormetadata derived from the medical report or medical scan.

The systematic factor identifier 4350 and the cognitive factoridentifier 4370 can operate via an inference function, via a statisticalanalysis, hypothesis test, clustering algorithm, generate-and-testalgorithm, search-based algorithm, a convolutional neural network,stacking neural network, a generative adversarial network, and/or othermachine learning algorithm that operated based on data fromretrospective discrepancy notifications and optionally other medicalreports without errors to generate factor data that indicates one ormore systematic, cognitive or other factors that correlate to theparticular discrepancies or other errors presented by the retrospectivediscrepancy notifications.

In various embodiments, the systematic factors identified can indicateerrors that occur more frequently for reviews associated with using aparticular one of plurality of PACS viewers and/or other viewing toolsand/or using a particular interface feature of a viewing tool. Infurther examples, a systemic error can be identified for reviews at aparticular time a day such as early morning, mid-morning, immediatelybefore or after lunch time, mid-afternoon, dinnertime, and or late atnight when a dip in attention span could occur. In further examples, asystemic error can be identified based on a number of prior reviews in areviewing session of a particular medical professional, for example,after 12 consecutive reviews or after a particular duration of thereviewing session, e.g., after two consecutive hours.

In various embodiments, the cognitive factors identified can indicateerrors that occur more frequently for reviews associated with ananchoring bias factor where the medical professional depends too heavilyon an initial piece of information offered, such as a prior diagnosis,preliminary diagnosis or review request from a referring physician orother medical professional, e.g. check for possible kidney stones. Thecognitive factors identified can indicate errors that occur morefrequently for reviews associated with a framing bias factor where themedical professional decides on options based on whether the options arepresented with positive or negative connotations, e.g. a bias againstthe diagnosis of a very severe condition. The cognitive factorsidentified can indicate errors that occur more frequently for reviewsassociated with a satisfaction of search factor or a satisfaction ofreport factor where the reviewer stops looking for abnormalities afterone or more abnormalities are already found. The cognitive factorsidentified can indicate errors that occur more frequently for reviewsassociated with a tunnel vision factor that indicates, for example, acognitive confirmation bias, hindsight bias, and/or outcome bias.

In various embodiments, the systematic factor identifier 4350 and thecognitive factor identifier 4370 operate to track trends across medicalscans, medical professionals, and/or medical institutions in theretroactive review and/or in flagging medical scans, medicalprofessionals, and/or medical institutions. For example, the factordetection system 4300 can determine trends that correlate to a highernumber, proportion, or frequency of medical scans flagged viaretrospective discrepancy notifications. For example, the factordetection system 4300 can track these trends across types of medicalscans, anatomical regions of medical scans, particular attributes ofpatient history, different geographic regions, qualifications orbackgrounds of medical professionals and/or medical institutions, and/orother attributes mapped to medical scans or medical professionals, forexample, in medical scan entries 352 or user profile entries 354. Forexample, the factor detection system 4300 can identify a geographicregion where a particular scan type or modality is flagged for reviewmore frequently.

FIG. 16 presents a flowchart illustrating a method in accordance withvarious embodiments. Step 3702 includes receiving, via a receiver, aplurality of medical scans and a plurality of medical labelscorresponding to the plurality of medical scans, wherein each of theplurality of medical labels correspond to one of a set of abnormalityclasses. Step 3704 includes generating, via a processor, a computervision model by training on the plurality of medical scans and theplurality of medical labels. Step 3706 includes receiving, via thereceiver, a new medical scan. Step 3708 includes generating, via theprocessor, probability matrix data by performing an inference functionthat utilizes the computer vision model on the new medical scan, whereinthe probability matrix data includes, for each of a set of image patchesof the new medical scan, a set of patch probability values correspondingto the set of abnormality classes, and wherein each of the set of patchprobability values indicates a probability that a corresponding one ofthe set of abnormality classes is present in the each of the set ofimage patches. Step 3710 includes generating, via the processor,preliminary heat map visualization data based on the probability matrixdata. Step 3712 includes generating, via the processor, heat mapvisualization data by a post-processing of the preliminary heat mapvisualization data to mitigate heat map artifacts. Step 3714 includestransmitting, via a transmitter, the heat map visualization data to aclient device for display via a display device.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may further be used herein, the term “associatedwith”, includes direct and/or indirect coupling of separate items and/orone item being embedded within another item. As may still further beused herein, the term “automatically” refers to an action causeddirectly by a processor of a computer network in response to atriggering event and particularly without human interaction.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing device” and/or “processing unit” maybe a single processing device or a plurality of processing devices. Sucha processing device may be a microprocessor, micro-controller, digitalsignal processor, graphics processing unit, microcomputer, centralprocessing unit, field programmable gate array, programmable logicdevice, state machine, logic circuitry, analog circuitry, digitalcircuitry, and/or any device that manipulates signals (analog and/ordigital) based on hard coding of the circuitry and/or operationalinstructions. The processing module, module, processing circuit, and/orprocessing unit may be, or further include, memory and/or an integratedmemory element, which may be a single memory device, a plurality ofmemory devices, and/or embedded circuitry of another processing module,module, processing circuit, and/or processing unit. Such a memory devicemay be a read-only memory, random access memory, volatile memory,non-volatile memory, static memory, dynamic memory, flash memory, cachememory, and/or any device that stores digital information. Note that ifthe processing module, module, processing circuit, and/or processingunit includes more than one processing device, the processing devicesmay be centrally located (e.g., directly coupled together via a wiredand/or wireless bus structure) or may be distributedly located (e.g.,cloud computing via indirect coupling via a local area network and/or awide area network). Further note that if the processing module, module,processing circuit, and/or processing unit implements one or more of itsfunctions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, and/or processing unit executes, hard coded and/oroperational instructions corresponding to at least some of the stepsand/or functions illustrated in one or more of the Figures and/ordescribed herein. Such a memory device or memory element can be includedin an article of manufacture. While the processing module, module,processing circuit, and/or processing unit device may be a generalpurpose computing device, the execution of the hard coded and/oroperational instructions by the processing module, module, processingcircuit, and/or processing unit configures such a general purposecomputing device as a special purpose computing device to implement thecorresponding steps and/or functions illustrated in one or more of theFigures and/or described herein. In particular, the hard coded and/oroperational instructions by the processing module, module, processingcircuit, and/or processing unit implement acts and algorithms performedby the processing module, module, processing circuit, and/or processingunit. Such acts and algorithms can be identified by name, can beillustrated via flowchart and/or described in words.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

The term “system” is used in the description of one or more of theembodiments. A system implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A system may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a system may containone or more sub-system, each of which may be one or more systems.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A multi-label heat map generating system,comprising: at least one processor; and a memory that stores operationalinstructions that, when executed by the at least one processor, causethe multi-label heat map generating system to: receive, via a receiver,a plurality of medical scans and a plurality of medical labelscorresponding to the plurality of medical scans, wherein each of theplurality of medical labels correspond to one of a set of abnormalityclasses; generate a computer vision model by training on the pluralityof medical scans and the plurality of medical labels; receive, via thereceiver, a new medical scan; generate probability matrix data byperforming an inference function that utilizes the computer vision modelon the new medical scan, wherein the probability matrix data includes,for each of a set of image patches of the new medical scan, a set ofpatch probability values corresponding to the set of abnormalityclasses, and wherein each of the set of patch probability valuesindicates a probability that a corresponding one of the set ofabnormality classes is present in the each of the set of image patches;generate preliminary heat map visualization data based on theprobability matrix data; generate heat map visualization data by apost-processing of the preliminary heat map visualization data tomitigate heat map artifacts; transmit, via a transmitter, the heat mapvisualization data to a client device for display via a display device,wherein an interface displayed by the display device displays each of aset of heat maps indicated in the heat map visualization data, andwherein each of the set of heat maps corresponds to the set ofabnormality classes, wherein the interface is an interactive userinterface that responds to actions of a medical professional; receivefactor data that identifies one or more factors that contribute toerrors associated with the medical professional; and determine a customheat map setting, based on the one or more factors that contribute toerrors associated with the medical professional, wherein thepost-processing of the preliminary heat map visualization data is inaccordance the custom heat map setting.
 2. The multi-label heat mapgenerating system of claim 1, wherein the heat map visualization dataassigns, for each one of the set of abnormality classes, one color valueof a set of color values for portions of the new medical scan where theone of the set of abnormality classes is present.
 3. The multi-labelheat map generating system of claim 2, wherein the one color value ofthe set of color values is predetermined based on a severity associatedwith the one of the set of abnormality classes.
 4. The multi-label heatmap generating system of claim 2, wherein the one color value of the setof color values is assigned based on a confidence associated with aseverity associated the one of the set of abnormality classes.
 5. Themulti-label heat map generating system of claim 2, wherein thepost-processing of the preliminary heat map visualization data includes:determining boundaries corresponding to the portions of the preliminaryheat map visualization data where the one of the set of abnormalityclasses is present; and blurring the boundaries in the preliminary heatmap visualization data.
 6. The multi-label heat map generating system ofclaim 2, wherein the post-processing of the preliminary heat mapvisualization data includes: determining boundaries corresponding to theportions of the preliminary heat map visualization data where the one ofthe set of abnormality classes is present; and filtering the boundariesto reduce tails and sharp edges.
 7. The multi-label heat map generatingsystem of claim 2, wherein the post-processing of the preliminary heatmap visualization data includes: determining boundaries corresponding tothe portions of the preliminary heat map visualization data where theone of the set of abnormality classes is present; and filtering outlierpoints outside of the boundaries in the preliminary heat mapvisualization data.
 8. The multi-label heat map generating system ofclaim 7, wherein filtering the outlier points includes: applying asegmentation mask to mask pixels of the preliminary heat mapvisualization data that are outside of the boundaries, wherein thepixels that are outside the boundaries are not assigned color values inthe heat map visualization data.
 9. The multi-label heat map generatingsystem of claim 1, wherein the post-processing of the preliminary heatmap includes: comparing the probability that the corresponding one ofthe set of abnormality classes is present in the each of the set ofimage patches with a probability threshold; and only highlighting animage patch in the set of image patches when the probability comparesfavorably to the probability threshold.
 10. The multi-label heat mapgenerating system of claim 1, wherein the operational instructions, whenexecuted by the at least one processor, further cause the multi-labelheat map generating system to: generating global probability data basedon the probability matrix data, wherein the global probability dataindicates a set of global probability values corresponding to the set ofabnormality classes, wherein each of the set of global probabilityvalues indicates a probability that a corresponding one of the set ofabnormality classes is present in the new medical scan; and generatingheat map ordering data by ranking the global probability data, whereinthe interface displays the set of heat maps in an order indicated by theheat map ordering data.
 11. The multi-label heat map generating systemof claim 10, wherein one of the set of heat maps corresponding to one ofthe set of abnormality classes with a highest corresponding globalprobability value is displayed first.
 12. The multi-label heat mapgenerating system of claim 1, wherein the operational instructions, whenexecuted by the at least one processor, further cause the multi-labelheat map generating system to: determining a subset of the set ofabnormality classes are present in the new medical scan based on theprobability matrix data; wherein the interface only displays heat mapscorresponding to the subset of the set of abnormality classes.
 13. Themulti-label heat map generating system of claim 1, wherein theoperational instructions, when executed by the at least one processor,further cause the multi-label heat map generating system to: respond touser interactions with the interactive user interface to toggle betweendisplays of two or more of the set of heat maps.
 14. The multi-labelheat map generating system of claim 1, wherein the operationalinstructions, when executed by the at least one processor, further causethe multi-label heat map generating system to: respond to userinteractions with the interactive user interface to select a subset ofthe set of heat maps to be displayed in association with a futureprocessing of another new medical scan by the multi-label heat mapgenerating system.
 15. The multi-label heat map generating system ofclaim 1, wherein the one or more factors include at least one systematicfactor that indicates errors that occur more frequently for reviews bythe medical professional associated with: using a particular one ofplurality of viewing tools; using a particular interface feature of aviewing tool; a particular time a day; after a number of prior reviewsin a reviewing session of the medical profession; or after a particularduration of the reviewing session.
 16. The multi-label heat mapgenerating system of claim 1, wherein the one or more factors include atleast one of: an anchoring bias factor; a framing bias factor; asatisfaction of search factor; a satisfaction of report factor; or atunnel vision factor.
 17. A method, comprising: receiving, via areceiver, a plurality of medical scans and a plurality of medical labelscorresponding to the plurality of medical scans, wherein each of theplurality of medical labels correspond to one of a set of abnormalityclasses; generating, via a processor, a computer vision model bytraining on the plurality of medical scans and the plurality of medicallabels; receiving, via the receiver, a new medical scan; generating, viathe processor, probability matrix data by performing an inferencefunction that utilizes the computer vision model on the new medicalscan, wherein the probability matrix data includes, for each of a set ofimage patches of the new medical scan, a set of patch probability valuescorresponding to the set of abnormality classes, and wherein each of theset of patch probability values indicates a probability that acorresponding one of the set of abnormality classes is present in theeach of the set of image patches; generating, via the processor,preliminary heat map visualization data based on the probability matrixdata; generating, via the processor, heat map visualization data by apost-processing of the preliminary heat map visualization data tomitigate heat map artifacts; transmitting, via a transmitter, the heatmap visualization data to a client device for display via a displaydevice, wherein an interface displayed by the display device displayseach of a set of heat maps indicated in the heat map visualization data,and wherein each of the set of heat maps corresponds to the set ofabnormality classes; generating global probability data based on theprobability matrix data, wherein the global probability data indicates aset of global probability values corresponding to the set of abnormalityclasses, wherein each of the set of global probability values indicatesa probability that a corresponding one of the set of abnormality classesis present in the new medical scan; and generating heat map orderingdata by ranking the global probability data, wherein the interfacedisplays the set of heat maps in an order indicated by the heat mapordering data.
 18. The method of claim 17, wherein the heat mapvisualization data assigns, for each one of the set of abnormalityclasses, one color value of a set of color values for portions of thenew medical scan where the one of the set of abnormality classes ispresent.
 19. The method of claim 18, wherein the one color value of theset of color values is predetermined based on a severity associated withthe one of the set of abnormality classes.
 20. The method of claim 18,wherein the one color value of the set of color values is assigned basedon a confidence associated with a severity associated the one of the setof abnormality classes.